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"ABSTRAK<>br>Dalam beberapa tahun terakhir, masalah pengukuran kredibilitas informasi di jaringan sosial mendapat perhatian yang cukup besar terutama di bawah situasi darurat. Hal itu merupakan konsekuensi dari membeludaknya informasi, terlebih ketika semua orang bebas berperan sebagai sumber informasi.Penelitian ini menyoroti buramnya dinding pembatas antara fakta dan hoax di Indonesia, sehingga hal itu menyebabkan banyaknya kasus penyebaran hoax di media. Jika dibiarkan hal tersebut dapat berdampak buruk bagi seorang pribadi ataupun organisasi yang diserang isu hoax. Survei yang dilakukan Intelligence Media Management IMM menyatakan terdapat peningkatan tajam di tahun 2016 dari 1572 menjadi 7311 pemberitaan media. Dan berdasarkan hasil survei yang dilakukan masyarakat telematika mastel Indonesia hampir dari seluruh responden 84,5 menyatakan terganggu dengan maraknya berita hoax yang dapat mengganggu kerukunan masyarakat dan menghambat pembangunan nasional.Menurut Menteri Komunikasi dan Informatika Rudiantara, langkah nyata yang bisa dilakukan adalah menyaring informasi menjadi lebih cepat dan tegas. Untuk itu diperlukan tindakan sehingga penyebaran hoax di media dapat diturunkan. Tujuan dilakukannya penelitian ini adalah untuk mengidentifikasi konten di media sosial merupakan suatu hoax atau tidak pada saat konten tersebut beredar. Metodologi yang digunakan di dalam penelitian ini dimulai dengan mengumpulkan tweets yang terindikasi hoax lalu dilakukan proses pengolahan data dengan membuat suatu model text mining yang dapat memprediksi suatu konten berpotensi hoax atau tidak.Hasil dari penelitian ini yaitu didapatkan sebuah model berbasis pembelajaran sendiri menggunakan algoritma LinearSVC dengan akurasi 91 yang dapat memprediksi apakah suatu tweet merupakan berpotensi hoax atau tidak sehingga membantu dalam menyaring suatu informasi yang diharapkan dapat mengurangi penyebaran hoax di Indonesia.

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ABSTRACT<>br>In recent years, the problem of measuring the credibility of information on the social network received considerable attention, especially under emergency situations. This is the consequence of too many information, especially when everyone is free to act as a source of information.The study highlights the blurring of the dividing wall between fact and hoax in Indonesia, so it causes many cases of spread of hoaxes in the media. If left unchecked it can be bad for a person or organization that attacked the issue of hoaxes. Surveys conducted by Intelligence Media Management IMM said there is a sharp increase in 2016 from 1572 content into 7311 content spread in media. And based on the results of a survey conducted by telematics community Mastel Indonesia almost of all respondents 84.5 declared disturbed by the rise of the hoax news that could disturb social harmony and impede national development.According to the Minister of Communications and Information Rudiantara, concrete steps that can be done is to filter information faster and firmer. It required the action so that the spread of hoax in the media can be derived. The purpose of this research is to identify content in social media is a hoax or not when the content is spreading. The methodology used in this research begins with collecting tweets that indicated hoax and then performed data processing by creating a text mining model that can predict a potentially hoax content or not.The result of this research is a machine learning model using LinearSVC algorithm with 91 accuracy which can predict whether tweet potentially hoax or not, thus helping the filtering of information expected to reduce the spread of hoax in Indonesia."

"[ABSTRAKAplikasi Fajr merupakan aplikasi mobile yang memiliki konten islami denganfitur utama yaitu Fajr Cards. Namun, Fajr Cards belum mampu menarikperhatian pengguna dengan minimnya jumlah pengguna fitur ini. Fajr Cardssebagai fitur yang berbasiskan kepada konten dapat ditingkatkan denganmemberikan konten yang relevan dengan pengguna. Twitter sebagai media sosialmemiliki data real-time dan jumlah yang banyak sehingga dapat menjadi sumberdata aktual untuk dianalisa. Data Twitter dapat dianalisa dengan menggunakantext mining. Salah satunya yaitu text classification atau klasifikasi teks Tujuanpenelitian ini adalah untuk menentukan metode klasifikasi apa yang terbaik untuk klasifikasi tema konten Fajr Cards. Metodologi yang digunakan menggunakan tahapan preprocess Text Mining danpenggunaan metode Text Mining yaitu Text Classification. Hasil yang diharapkan adalah gambaran bagaimana data Twitter diproses untuk proses klasifikasi dan metode klasifikasi apa yang terbaik untuk klasifikasi tema konten Fajr Cards.

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ABSTRACTFajr application is a mobile application that contains Islamic contents for moslem daily life. To get more users, the developers create a main feature called Fajr Cards. But, Fajr Cards has not been able to attract users. It is based on the minimum of users that using Fajr Cards. Fajr Cards as a feature based on contents can be improved by adding more content that have relevance value to users. Twitter as microblog social media have real time and a lot of data. Those data can be used as an actual source data for analyze. Text mining such as text classification will be used to analyze the data. The purpose of this research is to get what classification method that suited best for this classification. Methodology that used in this research is Text Mining including preprocess and Text Classification. The expected results is to know what classification method that suited best for Fajr Card?s theme classification.;Fajr application is a mobile application that contains Islamic contents for moslemdaily life. To get more users, the developers create a main feature called FajrCards. But, Fajr Cards has not been able to attract users. It is based on theminimum of users that using Fajr Cards. Fajr Cards as a feature based on contentscan be improved by adding more content that have relevance value to users.Twitter as microblog social media have real time and a lot of data. Those data canbe used as an actual source data for analyze. Text mining such as textclassification will be used to analyze the data. The purpose of this research is toget what classification method that suited best for this classification.Methodology that used in this research is Text Mining including preprocess andText Classification. The expected results is to know what classification method that suited best for Fajr Card?s theme classification.;Fajr application is a mobile application that contains Islamic contents for moslemdaily life. To get more users, the developers create a main feature called FajrCards. But, Fajr Cards has not been able to attract users. It is based on theminimum of users that using Fajr Cards. Fajr Cards as a feature based on contentscan be improved by adding more content that have relevance value to users.Twitter as microblog social media have real time and a lot of data. Those data canbe used as an actual source data for analyze. Text mining such as textclassification will be used to analyze the data. The purpose of this research is toget what classification method that suited best for this classification.Methodology that used in this research is Text Mining including preprocess andText Classification. The expected results is to know what classification method that suited best for Fajr Card?s theme classification., Fajr application is a mobile application that contains Islamic contents for moslemdaily life. To get more users, the developers create a main feature called FajrCards. But, Fajr Cards has not been able to attract users. It is based on theminimum of users that using Fajr Cards. Fajr Cards as a feature based on contentscan be improved by adding more content that have relevance value to users.Twitter as microblog social media have real time and a lot of data. Those data canbe used as an actual source data for analyze. Text mining such as textclassification will be used to analyze the data. The purpose of this research is toget what classification method that suited best for this classification.Methodology that used in this research is Text Mining including preprocess andText Classification. The expected results is to know what classification method that suited best for Fajr Card’s theme classification.]"

"Pembentukan RUU Cipta Kerja memunculkan berbagai macam polemik di Indonesia. Penolakan terhadap RUU tersebut ditunjukkan oleh masyarakat Indonesia dengan berbagai cara. Mulai dari diskusi dengar pendapat dengan DPR, membahas dan mengangkat isu-isu kontroversial dalam RUU tersebut di berbagai media sosial, bahkan sampai melakukan demonstrasi besar-besaran yang tidak jarang berakhir dengan kericuhan. Penelitian ini bertujuan untuk mengidentifikasi stance masyarakat terhadap RUU Cipta kerja pada media sosial Twitter. Dataset diambil dari Twitter menggunakan kata kunci terkait RUU Cipta Kerja sebanyak 9440 data Tweet dalam periode 25 Oktober 2019 sampai pada 25 Oktober 2020. Anotasi dilakukan menggunakan label PRO, ANTI, ABS, dan IRR. Eksperimen yang dilakukan mengguanakan fitur unigram, bigram, dan unigram+bigram, dengan algoritma Multinomial Naïve Bayes, Support Vector Machine, dan Logistic Regression. Model terbaik dari eksperimen tersebut adalah model yang menggunakan fitur unigram dengan menggunakan algoritma klasifikasi logistic regression yang dapat mencapai nilai micro f-1 score sebanyak 72,3%.

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The formation of RUU Cipta Kerja (Job creation law) gave rise to various kinds of polemics in Indonesia. The Indonesian people have shown rejection of the law in various ways. Starting from hearing discussions with the DPR, discussing and raising controversial issues in the law on various social media, even holding large demonstrations that often end in chaos. This study aims to identify the public's stance on the job creation law on Twitter social media. The dataset was taken from Twitter using keywords related to the job creation law, totaling 9440 Tweets from 25 October 2019 to 25 October 2020. Annotations were carried out using the PRO, ANTI, ABS, and IRR labels. The experiments were carried out using unigram, bigram, and unigram + bigram features, with the Naïve Bayes Multinomial algorithm, Support Vector Machine, and Logistic Regression. The best model of the experiment is a model that uses the unigram feature using the logistic regression classification algorithm which can achieve a micro f-1 score of 72,3%."

"[ABSTRAKBanyaknya jejaring sosial yang bermunculan. Salah satu jejaring sosial yang marak digunakan adalah twitter. Kegiatan promosi produk melalui twitter sudah mulai digunakan PT XL Axiata Tbk (XL) sejak tahun 2009 melalui akun @XL123. Penggunaan twitter oleh perusahaan telekomunikasi di Indonesia masih dalam tahap penjualan dan promosi.Namun demikian, analisis yang dilakukan hanya terbatas pada perhitungan jumlah retweet, komentar, dan follower. Analisis belum melihat sejauh mana makna komentar dari pelanggan maupun masyarakat. Hal ini akan mempengaruhi keputusan membeli masyarakat jika komentar yang diberikan negatif, dan sebaliknya komentar positif akan meningkatkan citra perusahaan di mata stakeholder. Hal ini dapat dilihat dari fakta bahwa rating yang diperoleh XL Axiata tidak sesuai dengan ekspektasi brand tersebut, yaitu rating 3 dari 10. Sedangkan ekspektasi yang diharapkan berdasarkan analisa perbandingan jumlah follower dan following, semestinya XL Axiata memiliki reputasi yang bagus yaitu 7-8.Penelitian ini melakukan perhitungan reputasi dari produk XL Axiata, dan membandingkannya dengan produk Telkomsel dan Indosat. Selanjutnya dilakukan beberapa teknik ekstrak data, analisis sentimen, serta membandingkan tiga algoritma klasifikasi: Naïve Bayes, Support Vector Machine, dan Decision Tree. Tahap evaluasi performansi menggunakan precision, recall, f-measure, dan kurva ROC (AUC). Hasil menunjukkan bahwa model yang dibentuk oleh SVM memberikan performansi yang lebih baik untuk selanjutnya digunakan untuk melakukan perhitungan Net Brand Reputation. Perhitungan NBR dilakukan di produk 3G, 4G, Voice, SMS, dan Internet (data). Berdasarkan perbandingan kelima produk ini, XL Axiata memperoleh rata-rata nilai reputasi yang lebih di dibandingkan Telkomsel dan Indosat yaitu sebesar 24.5%, sedangkan Telkomsel hanya memperoleh 13.2% dan Indosat 19.3%.

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ABSTRACTThe internet in Indonesia has grown rapidly, it proved by many social media comes up. One of famous social media is twitter. Campaign product using twitter had been used by XL Axiata since 2009 through account @XL123. Unfortunately, the using of twitter in Indonesia telecommunication company still in the stage of sales and promotions.However, the analysis only calculated number of retweets, comments, dan followers. Analyzes haven?t seen what is the meaning of those comments, whether be positive or negative for XL brand products. Negative comments giving influence to society buying decision, while positive comments create good reputation to stakeholders. This is showned by a fact that the rating obtained XL Axiata does not correspond to the brand?s expectation, ie rating 3 out of 10. While expectation based on comparative analysis of number of followers and following, XL Axiata should have a good reputation in rate 7-8.This study not only calculating XL product but also Telkomsel and Indosat for comparative analysis. Hereafter, we extracted features, algorithms and the classification schemes. Evaluation phase using precision, recall, f-measure and ROC curve (AUC). The sentiments are classified and compared using three different algorithms: Naïve Bayes, Support Vector Machine, and Decision Tree classifier method. The result shows model built by SVM is the best result. Using this model, we measure Net Brand Reputation in 5 products which are 3G, 4G, Voice, SMS, and Internet (data). The experiments showned XL Axiata has the highest reputation score rather than Telkomsel and Indosat with average NBR score 24,5%, while Telkomsel only 13.2% and Indosat 19.3%.;The internet in Indonesia has grown rapidly, it proved by many social media comes up. One of famous social media is twitter. Campaign product using twitter had been used by XL Axiata since 2009 through account @XL123. Unfortunately, the using of twitter in Indonesia telecommunication company still in the stage of sales and promotions.However, the analysis only calculated number of retweets, comments, dan followers. Analyzes haven?t seen what is the meaning of those comments, whether be positive or negative for XL brand products. Negative comments giving influence to society buying decision, while positive comments create good reputation to stakeholders. This is showned by a fact that the rating obtained XL Axiata does not correspond to the brand?s expectation, ie rating 3 out of 10. While expectation based on comparative analysis of number of followers and following, XL Axiata should have a good reputation in rate 7-8.This study not only calculating XL product but also Telkomsel and Indosat for comparative analysis. Hereafter, we extracted features, algorithms and the classification schemes. Evaluation phase using precision, recall, f-measure and ROC curve (AUC). The sentiments are classified and compared using three different algorithms: Naïve Bayes, Support Vector Machine, and Decision Tree classifier method. The result shows model built by SVM is the best result. Using this model, we measure Net Brand Reputation in 5 products which are 3G, 4G, Voice, SMS, and Internet (data). The experiments showned XL Axiata has the highest reputation score rather than Telkomsel and Indosat with average NBR score 24,5%, while Telkomsel only 13.2% and Indosat 19.3%., The internet in Indonesia has grown rapidly, it proved by many social media comes up. One of famous social media is twitter. Campaign product using twitter had been used by XL Axiata since 2009 through account @XL123. Unfortunately, the using of twitter in Indonesia telecommunication company still in the stage of sales and promotions.However, the analysis only calculated number of retweets, comments, dan followers. Analyzes haven’t seen what is the meaning of those comments, whether be positive or negative for XL brand products. Negative comments giving influence to society buying decision, while positive comments create good reputation to stakeholders. This is showned by a fact that the rating obtained XL Axiata does not correspond to the brand’s expectation, ie rating 3 out of 10. While expectation based on comparative analysis of number of followers and following, XL Axiata should have a good reputation in rate 7-8.This study not only calculating XL product but also Telkomsel and Indosat for comparative analysis. Hereafter, we extracted features, algorithms and the classification schemes. Evaluation phase using precision, recall, f-measure and ROC curve (AUC). The sentiments are classified and compared using three different algorithms: Naïve Bayes, Support Vector Machine, and Decision Tree classifier method. The result shows model built by SVM is the best result. Using this model, we measure Net Brand Reputation in 5 products which are 3G, 4G, Voice, SMS, and Internet (data). The experiments showned XL Axiata has the highest reputation score rather than Telkomsel and Indosat with average NBR score 24,5%, while Telkomsel only 13.2% and Indosat 19.3%.]"

"ABSTRAKPangan merupakan hak asasi manusia yang harus senantiasa terpenuhi oleh masyarakat dengan daya beli yang sesuai dan mempunyai kualitas pangan yang tinggi dan aman. Hal tersebut mendorong kedaulatan pangan suatu negara, yang secara mandiri memenuhi kebutuhan pangan masyarakatnya berdasarkan sistem pangan yang adil bagi seluruh masyarakat. Peraturan Pemerintah Republik Indonesia Nomor 17 Tahun 2015 yang mewajibkan adanya sistem informasi tentang pangan dan gizi dan teori evolusi e-government 3.0. Oleh karena itu salah satu solusi yang mendukung peraturan tersebut dan pendekatan e-government 3.0 adalah dengan pendekatan text mining. Penelitian ini mengolah data dari LAPOR! dan berita daring mengenai kedaulatan pangan untuk mengekstrak informasi dan menemukan pola-pola yang akan menghasilkan informasi tentang kedaulatan pangan di Indonesia sehingga dapat membantu pengambilan keputusan yang berdasar pada data melalui representasi visualisasi berbasis web. Jenis analisis informasi yang digunakan adalah Klasifikasi Dokumen untuk penyaringan dokumen, Named Entitiy Recognition yang digunakan untuk mengetahui entitas lokasi dan komoditas pangan dari data tekstual, dan Topic Modelling untuk menemukan topik dari sekumpulan teks dokumen berita dan aduan LAPOR!. Algoritma yang dipakai dalam penelitian ini adalah Conditional Random Fields dan Conditional Markov Model untuk implementasi Named Entity Recognition. Latent Dirichlet Allocation dan Non-Negative Matrix Factorization untuk implementasi Topic Modelling. Selain itu Naïve Bayes, Support Vector Machine, dan Logistic Regression digunakan untuk klasifikasi dokumen. Sedangkan pemilihan model ini menggunakan Conditional Random Field dengan nilai F1-score pada entitas lokasi sebesar 83.85 dan entitas komoditas pangan sebesar 90.98 yang digunakan pada data berita daring, pada data aduan LAPOR!, entitas lokasi menggunakan Conditional Markov Model dengan nilai F1-Score sebesar 60.35 dan entitas komoditas pangan sebesar 89.74. Pada klasfikasi dokumen, model Support Vector Machine dengan fitur unigram memiliki nilai presisi sebesar 92.00. Pada Topic Modelling, model Non-Negative Matrix Factorization memiliki nilai coherence yang lebih tinggi daripada Latent Direchlete Allocation pada tiga eksperimen dengan dataset yang berbeda. Di samping itu, dilakukan visualisasi tentang kedaulatan pangan berdasarkan pengolahan data tersebut di atas untuk memudahkan pengambilan kebijakan oleh pimpinan seperti Tim Ahli di Kantor Staf Presiden.

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ABSTRACTFood is a human right that must always be fulfilled by the society with the appropriate purchasing power and high and safe food quality. This encourages food sovereignty of a country, which independently meets the food needs of its people based on a food system that is fair to the entire community. Peraturan Pemerintah Republik Indonesia Nomor 17 Tahun 2015 requires an information system on food and nutrition and the theory of e-government 3.0 evolution. Therefore, one solution that supports these regulations and the e-government 3.0 approach is the text mining approach. This research processes data from LAPOR! and online news on food sovereignty to extract information and find patterns that will produce information on food sovereignty in Indonesia so that it can assist decision-making based on data through web-based visualization representation. The type of information analysis used is Document Classification for document filtering, Named Entity Recognition which is used to find out location entities and food commodities from textual data, and Topic Modeling to find topics from a collection of text news documents and complaints LAPOR !. The algorithm used in this study is Conditional Random Fields and Conditional Markov Models for the implementation of Named Entity Recognition. Latent Dirichlet Allocation and Non-Negative Matrix Factorization for the implementation of Topic Modeling. In addition Naïve Bayes, Support Vector Machine, and Logistic Regression are used for document classification. Whereas the selection of this model uses Conditional Random Field with an F1-score value for location entities of 83.85 and a food commodity entity of 90.98 used in online news data. In the LAPOR! Complaint data, the location entity uses Conditional Markov Model with an F1-Score value of 60.35 and food commodity entities amounting to 89.74. In classifying documents, the Support Vector Machine model with unigram features has a precision value of 92.00. In Topic Modeling, the Non-Negative Matrix Factorization model has a higher coherence value than the Latent Direchlete Allocation in three experiments with different datasets. In addition, visualization of food sovereignty is based on the processing of the data above to facilitate policy making by leaders such as the Expert Team at the Kantor Staf Presiden.

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"PT. Indosat Tbk adalah salah satu perusahaan yang berkembang pada industri telekomunikasi. Namun, PT. Indosat Tbk memiliki permasalahan mengenai customer satisfaction yang cenderung menurun dari tahun ke tahun. Data media sosial, terutama twitter, menawarkan data mengenai opini publik yang sangat padat. Namun data twitter yang masih bersifat unstructured diperlukan proses lebih lanjut untuk dapat menemukan dimensi-dimensi beserta sentimen masyarakat terhadap dimensi tersebut. Latent Dirichlet Allocation (LDA) dengan Generative Statistical modelnya memungkinkan suatu set data pengamatan dapat dijelaskan oleh kelompok yang tidak teramati. Penelitian ini menentukan 30 kelompok kata representatif dari hasil LDA. Hasilnya terdapat 18 dimensi yang paling banyak dibicarakan mengenai Indosat pada linimasa twitter. Dimensidimensi tersebut mewakili 14 dimensi yang sudah ditemukan pada penelitian-penelitian sebelumnya mengenai kepuasan pelanggan pada layanan telekomunikasi, bahkan dengan LDA mendapatkan dimensi lebih detail dan lebih real time. Masing-masing dokumen dalam dimensi tersebut diberi label sentimennya, dan ditentukan akurasinya menggunakan supervised classification, hasilnya adalah 72% akurasi dengan model Naive Bayes Classification. Mengabaikan sentimen netral, sentimen negatif Indosat masih lebih tinggi daripada sentimen positifnya, yaitu dengan 16% sentimen negatif. Persentase negatif tersebut masih didominasi dengan dimensi berkaitan dengan layanan Indosat. Sementara dominasi sentimen positif ada pada dimensi yang berhubungan dengan ketersediaan layanan untuk pengguna.

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PT. Indosat Tbk is One of the companies developing in the telecommunications industry. However, PT. Indosat Tbk is very concerned about customer satisfaction which tends to decrease from year to year. Social media media, especially Twitter, offer data about public opinion that is very crowded. However, the twitter data that is still unstructured requires a further process to be able to find the dimensions and sentiments of the community towards that dimension. Latent Dirichlet Allocation (LDA) with the Generative Statistics model allows a monitoring data set to be accessed by unobserved groups. This study determines 30 groups of words that represent the results of the LDA. There are 18 dimensions that are most talked about about Indosat on the Twitter timeline. These dimensions represent the 14 dimensions found in previous studies of customer satisfaction in telecommunications services, even with LDA getting more detailed and more real-time dimensions. Each document in this dimension is labeled sentiment, and its accuracy is determined using a supervised classification, obtained 72% accuracy with the Naive Bayes Classification model. Ignoring the negative sentiment, Indosat's negative sentiment was still higher than the positive sentiment, namely with a 16% negative sentiment. The negative percentage is still a comparison with Indosat services. While the dominance of positive sentiment is in the dimensions associated with service support for users."

"Investasi berbasis Crowdfunding merupakan Platform yang mengembangkan berbagai macam keunggulan yang mereka miliki untuk memikat masyarakat agar mau melakukan investasi digital, seperti menyediakan fitur berbagai aneka ragam instrumen investasi dan memberikan kemudahan seperti menawarkan biaya minimum untuk melakukan investasi sebagai modal awal. Penelitian ini bertujuan untuk mengetahui dan menganalisis ulasan pada aplikasi Crowdfunding Land X dan Santara dengan menggunakan metode Text Mining yang berbasis Sentiment Analysis Data yang digunakan dalam penelitian ini merupakan data sekunder yang didapat dengan cara mengambil data yang berupa text review pada aplikasi Crowdfunding Land X dan Santara. Data review yang berhasil diambil untuk aplikasi Santara sebesar 14.991 review, dan data pada aplikasi Land X, data yang berhasil berjumlah 2.241 review. Alat analisis yang digunakan dalam penelitian ini adalah software R dengan metode Text Mining berbasis Sentiment Analysis. Dengan menggunakan Text Mining berbasis Sentiment Analysis, dapat menjadi salah satu indicator analisis untuk melihat pandangan pengguna aplikasi terhadap aplikasi Land X dan Santara.

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Crowdfunding-based investments are platforms that develop many various advantages to entice the public to make digital investments, such as providing features for a wide variety of investment instruments and giving conveniences such as offering minimum fees for investing as initial capital. This study aims to find out and analyze reviews on Crowdfunding Land X and Santara applications using the Sentiment Analysisbased Text Mining method. The data used in this study is secondary data obtained by taking data in the form of text reviews on the Land X and Santara Crowdfunding applications. The successful review data was taken for the Santara application amounted to 14,991 reviews, and the data on the Land X application, the successful data amounted to 2,241 reviews. . The analytical tool used in this study is R software with the Text Mining method based on Sentiment Analysis. By using Text Mining based on Sentiment Analysis, it can be an indicator of analysis to see the views of application users on Land X and Santara applications."

"Kajian risiko bencana di Indonesia oleh BNPB menunjukkan jumlah jiwa terpapar risiko bencana tersebar di seluruh Indonesia dengan total potensi jiwa terpapar lebih dari 255 juta jiwa. Hasil kajian ini menunjukkan bahwa dampak bencana di Indonesia terbilang sangat tinggi. Sistem penanggulangan khususnya pada masa tanggap darurat menjadi hal yang krusial untuk dapat meminimalisir risiko. Namun, pemberian bantuan kepada korban bencana terkendala beberapa hal, antara lain keterlambatan dalam penyaluran, kurangnya informasi lokasi korban, dan distribusi bantuan yang tidak merata. Untuk memberikan informasi yang cepat dan tepat, BNPB telah membangun beberapa sistem informasi seperti DIBI, InAware, Geospasial, Petabencana.id dan InaRisk. Akan tetapi tidak secara realtime menampilkan wilayah terdampak bencana dengan memnunjukkan jenis kebutuhan bantuan apa yang dibutuhkan korban pada saat itu. Untuk memberikan solusi atas permasalah tersebut, penelitian ini membangun model yang mampu mengklasifikasikan data teks dari Twitter terkait bencana kedalam jenis bantuan yang diminta oleh korban bencana secara realtime. Selain itu visualisasi berupa dashboard dibangun dalam bentuk aplikasi berbasis peta untuk menampilkan lokasi korban yang terdampak. Penelitian ini mengunakan teknik text mining mengolah data Twitter dengan pendekatan metode klasifikasi multi label dan ekstraksi informasi lokasi menggunakan metode Stanford NER. Algoritme yang digunakan adalan Naive Bayes, Support Vector Machine, dan Logistic Regression dengan kombinasi metode tranformasi data multi label OneVsRest, Binary Relevance, Label Power-set, dan Classifier Chain. Representasi teks menggunakan N-Grams dengan pembobotan TF-IDF. Model terbaik untuk klasifikasi multi label pada penelitian ini adalah kombinasi Support Vector Machine dan Clasifier Chain dengan fitur UniGram+BiGram dengan nilai precision 82%, recall 70%, dan F1-score 75%. Stanford NER menghasilkan F1-score 83% untuk klasifikasi lokasi yang menjadi masukan untuk teknik geocoding. Hasil geocoding berupa informasi spasial ditampilkan dalam bentuk dashboard berbasis peta.

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The study of disaster risk in Indonesia by BNPB shows the number of people exposed to disaster risk throughout Indonesia with a total potential life of 255 million people. The results of this study indicate that the impact of disasters in Indonesia is quite high. The response system, especially during the emergency response period, is crucial to be able to minimize risks. However, providing assistance to disaster victims is hampered by several things, including delays in providing assistance, lack of information on the location of victims, and uneven distribution of aid. To provide fast and accurate information, BNPB has built several information systems such as DIBI, InAware, Geospatial, Petabencana.id and InaRisk. However, it does not display the disaster area in real-time by showing what kind of assistance needs the victim needs at that time. To provide a solution to these problems, this study builds a model that is able to classify text data from Twitter related to the type of assistance requested by disaster victims in real-time. In addition, a dashboard is built in the form of a map-based application to display the location of the realized victim. This study uses text mining techniques to process Twitter data with a multi-label classification approach and location information extraction using the Stanford NER method. The algorithms used are Naive Bayes, Support Vector Machine, and Logistic Regression with a combination of OneVsRest, Binary Relevance, Power-set Label, and Classifier Chain. Text representation using N-Grams with TF-IDF weighting. The best model for multi-label classification in this study is a combination of Support Vector Machine and Classifier Chain with UniGram+BiGram features with 82% precision, 70% recall, and 75% F1-score. Stanford NER produces an F1-score of 83% for location classification which is the input for geocoding techniques. Geocoding results in the form of spatial information are displayed in a map-based dashboard."

"Penerapan Teknologi Informasi dan Komunikasi (TIK) untuk meningkatkan keunggulan kompetitif.tidak hanya dimanfaatkan oleh sektor industri, namun juga sektor pemerintahan. Pemerintah Indonesia sendiri di dalam kaitannya dengan pemanfaatan TIK, telah membangun suatu sistem yang memungkinkan masyarakat untuk melaporkan keluhan dan aspirasinya melalui sistem LAPOR!. Sistem LAPOR! ciptaan pemerintah ini ternyata ditanggapi dengan antusias oleh masyarakat, terbukti dengan banyaknya laporan yang masuk ke pemerintah. Guna membantu kinerja pemerintah, dilakukan penelitian untuk menganalisis data tekstual laporan masyarakat dengan text mining untuk kemudian dilakukan disposisi otomatis ke dalam dua kategori utama LAPOR! yaitu topik dan instansi terkait. Disposisi otomatis dilakukan menggunakan teknik problem transformation pada multilabel classification melalui algoritma klasifikasi support vector machine dan naïve bayes. Hasil penelitian menunjukkan bahwa disposisi otomatis dapat diterapkan ke dalam sistem LAPOR! dan dapat meningkatkan kinerja disposisi laporan. Algoritma yang menghasilkan performa terbaik di dalam penerapannya adalah algoritma support vector machine

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The application of Information Technology and Communication (ICT) to escalate the competitive advantage is not only used in the industrial sector, but also in the government as well. The government of the Republic of Indonesia itsef, in the use of ICT, has built a system that enable its citizen to report their grievance and aspiration through LAPOR! system. This system turned out to be accepted with great enthusiasm by the public, as evidenced by the many reports to the government. In order to support the government’s performance, research is conducted to analyze the textual data using text mining, for later automatic disposition into two groups of LAPOR!'s category which is topik and instansi terkait. disposition is done using problem transformation technique in multilabel classification through support vector machine and naïve bayes classification algorithm. The result showed that automatic disposition can be applied into LAPOR! system and improves the report disposition’s performance. Algorithm that produces the best performance in the application is support vector machine. "

"Penelitian ini bertujuan untuk membangun proses bisnis pengawasan Fintech P2P Lending di Indonesia berbasis Berita Daring, Twitter, dan Ulasan Google Playstore. Usulan pengawasan yang baru digambarkan dengan Business Process Modeling Notation (BPMN). Selanjutnya diimplementasikan dengan membuat prototipe. Pendekatan yang digunakan adalah pendekatan Text Mining seperti ekstraksi informasi dengan Named Entity Recognition (NER), Analisis Sentimen dan Pemodelan Topik dengan Latent Dirichlet Allocation (LDA). Hasil eksperimen pada pendekatan NER menunjukan Algoritma Multinomial Naïve Bayes mendapatkan F1-score tertinggi sebesar 90%, sedangkan pada pendekatan analisis sentiment model Naïve Bayes dan Random Forest terbukti memiliki akurasi tinggi yaitu diatas 91%. Hasil NER membuktikan bahwa platform Cashless, Yokke, Digital Artha Media, Koinworks, Moka, Privy id, PT Tunaiku Fintech Indonesia, PT Relasi Perdana Indonesia, PT Dynamic Credit Asia dan PT Progo Puncak Group tidak ada dalam daftar Fintech di Otoritas Jasa Keuangan (OJK). Sedangkan hasil Persentase positif untuk aplikasi Adakami, Easycash, Danamas, Dompetkilat, dan Indodana berturut-turut adalah 47%, 59%, 28%, 24%, dan 29%. Penelitian ini dapat digunakan oleh OJK untuk pengawasan Fintech dan meningkatkan perlindungan konsumen.

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This research aims to build a business process to supervise Fintech P2P Lending in Indonesia based on Online News, Twitter, and Google Playstore Reviews. The proposed new supervision is described by the Business Process Modeling Notation (BPMN), then implemented by making a prototype. The Text Mining approach uses information extraction with Named Entity Recognition (NER), Sentiment Analysis, and Topic Modeling with Latent Dirichlet Allocation (LDA). Experimental results on the NER approach show that the Naïve Bayes Multinomial Algorithm gets the highest F1-score of 90%. In contrast, the Naïve Bayes and Random Forest model sentiment analysis approaches are proven to have high accuracy, above 91%. The NER results demonstrate that the platforms Cashless, Yokke, Digital Artha Media, Koinworks, Moka, Privy id, PT Tunaiku Fintech Indonesia, PT Relasi Perdana Indonesia, PT Dynamic Credit Asia, and PT Progo Puncak Group are not on the Fintech list at the Financial Services Authority (OJK). While the positive percentage results for the Adakami, Easycash, Danamas, Dompetkilat, and Indodana applications were 47%, 59%, 28%, 24%, and 29%, respectively. This research can be used by OJK for Fintech supervision and improving consumer protection."