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"Data clustering is one of the major areas in data mining. The bisecting clustering algorithm is one of the most widely used for high dimensional dataset. But its performance degrades as the dimensionality increases. Also, the task of selection of a cluster for further bisection is a challenging one. To overcome these drawbacks, we developed a novel partitional clustering algorithm called a HB-K-Means algorithm (High dimensional Bisecting K-Means). In order to improve the performance of this algorithm, we incorporate two constraints, such as a stability-based measure and a Mean Square Error (MSE) resulting in CHB-K-Means (Constraint-based High dimensional Bisecting K-Means) algorithm. The CHB-K-Means algorithm generates two initial partitions. Subsequently, it calculates the stability and MSE for each partition generated. Inference techniques are applied on the stability and MSE values of the two partitions to select the next partition for the re-clustering process. This process is repeated until K number of clusters is obtained. From the experimental analysis, we infer that an average clustering accuracy of 75% has been achieved. The comparative analysis of the proposed approach with the other traditional algorithms shows an achievement of a higher clustering accuracy rate and an increase in computation time."

"Data clustering is one of the major areas in data mining. The bisecting clustering algorithm is one of the most widely used for high dimensional dataset. But its performance degrades as the dimensionality increases. Also, the task of selection of a cluster for further bisection is a challenging one. To overcome these drawbacks, we developed a novel partitional clustering algorithm called a HB-K-Means algorithm (High dimensional Bisecting K-Means). In order to improve the performance of this algorithm, we incorporate two constraints, such as a stability-based measure and a Mean Square Error (MSE) resulting in CHB-K-Means (Constraint-based High dimensional Bisecting K-Means) algorithm. The CHB-K-Means algorithm generates two initial partitions. Subsequently, it calculates the stability and MSE for each partition generated. Inference techniques are applied on the stability and MSE values of the two partitions to select the next partition for the re-clustering process. This process is repeated until K number of clusters is obtained. From the experimental analysis, we infer that an average clustering accuracy of 75% has been achieved. The comparative analysis of the proposed approach with the other traditional algorithms shows an achievement of a higher clustering accuracy rate and an increase in computation time."

"Kecamatan Pomalaa, Provinsi Sulawesi Tenggara merupakan salah satu wilayah dengan dengan potensi cadangan mineral nikel yang banyak. Oleh karena itu, aktivitas pertambangan sangat sering dilakukan di daerah ini. Kegiatan pertambangan nikel sangat erat hubungannya dengan keselamatan kerja, terutama pada kegiatan produksi. Kegiatan produksi ini berkaitan juga dengan proses penggalian dan pengangkutan bahan galian. Salah satu area yang harus diperhatikan dalam kegiatan pertambangan adalah lereng area timbunan. Disposal area atau area timbunan adalah lokasi di daerah pertambangan yang dijadikan sebagai tempat penimbunan material-material overburden. Lereng disposal area yang tidak stabil akan berpengaruh terhadap kegiatan produksi. Karna itu diperlukan lokasi dan desain yang sesuai dalam membangun area timbunan. Penentuan lokasi area timbunan dan desain yang stabil dilakukan dengan menggunakan metode uji sifat fisik dan mekanika tanah, uji daya dukung tanah, dan analisis kesetimbangan batas menggunakan metode Janbu yang disederhanakan. Uji sifat fisik dan mekanika tanah pada penelitian ini dilakukan pada 1 titik dengan 4 buah sampel yang menyebar dari permukaan hingga kedalaman 3,20 meter. Nilai daya dukung tanah yang diperbolehkan dari peremukaan sampai pada kedalaman 2,99 meter memiliki nilai <81,812 ton/m2, sedangkan pada kedalaman 3,00­­–3,20 meter yang memiliki nilai daya dukung tanah yang diperbolehkan sebesar 435,81 ton/m2. penggalian tanah terlebih dahulu sedalam 3 meter disarankan sebelum dilakukan penimbunan, untuk mendapatkan area disposal yang aman sesuai dengan analisis uji daya dukung tanah. Hasil desain yang sesuai dengan area penelitian dibagi menjadi 2 section yaitu section A, dan section B dengan pembagian sudut section adalah 35°, 40°, dan 45°. Berdasarkan hasil analisis kesetimbangan batas dari masing-masing section dengan sudut yang sudah ditentukan, maka didapat bahwa pada section A dengan sudut kemiringan 45°; section B dengan sudut kemiringan 40°; dan 45° memiliki nilai FK < 1,3, desain lereng ini termasuk ke dalam lereng yang tidak aman dan berpotensi longsor. Sedangkan pada section A dengan sudut kemiringan 35°;40°; dan section B dengan sudut kemiringan 35° memiliki nilai FK > 1,3, Desain lereng ini termasuk ke dalam lereng yang aman dan memiliki kemungkinan longsor yang rendah. Berdasarkan analisis kesetimbangan batas, desain lereng yang aman digunakan memiliki kemiringan sudut 35°

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Pomalaa Sub-district, Southeast Sulawesi province is one of the areas with a high potential for nickel mineral reserves, Therefore, mining activities are very often carried out in this area. Nickel mining activities are closely related to work safety, especially in production activities. This production activity is also related to the process of extracting and transporting minerals. One area that must be considered in mining activities is the slope of the embankment area. A disposal or stockpiling area is a location in a mining area used as a place for stockpiling overburdened materials. Unstable slopes of the disposal area will affect production activities. Because of that, an appropriate location and design are needed in building a stockpile area. The determination of the location of the embankment area and its stable design is carried out by using the physical and mechanical properties of the soil, the soil bearing capacity test, and the boundary equilibrium analysis using the simplified Janbu method. Soil physical and mechanical properties tests in this study were carried out at 1 point with 4 samples that spread from the surface to a depth of 3.20 meters. The allowable soil carrying capacity from the surface to a depth of 2.99 meters has a value of <81.812 tons/m2, while at a depth of 3.00¬–3.20 meters, the allowable soil carrying capacity is 435.81 tons/ m2. Excavation of the soil as deep as 3 meters is recommended before backfilling, to obtain a safe disposal area according to the soil carrying capacity test analysis. The results of the design according to the research area are divided into 2 sections, namely section A and section B with the division angles of the sections being 35°, 40°, and 45°. Based on the results of the boundary equilibrium analysis of each section with a predetermined angle, it is found that section A with an inclination angle of 45°; section B with a tilt angle of 40°; and 45° has an FK value < 1.3, this slope design is classified as an unsafe slope and has the potential for landslides. Whereas in section A with a slope angle of 35°;40°; and section B with a slope angle of 35° has an FK value of > 1.3. This slope design is considered a safe slope and has a low probability of landslides. Based on the boundary equilibrium analysis, a safe slope design has a slope angle of 35°."

"Kilang minyak Jambi didirikan tahun 1945 dengan maksud untuk mengolah bahan bakar minyak yang ada di Jambi guna menyokong dan membantu mempertahankan Pemerintah RI yang bare merdeka."

"Pada penelitian ini mengimplementasikan algoritma Similarity Based Biclustering dengan menggunakan PAM clustering pada tiga dataset ekspresi gen microarray. Penelitian ini bertujuan untuk mengetahui ekspresi regulasi dari masing-masing bicluster yang diperoleh dan mengetahui kinerja algoritma Similarity Based Biclustering-PAM clustering berdasarkan hasil analisis kelompok kondisi. Similarity based biclustering-PAM clustering secara teoritis terdiri dari empat tahap utama yaitu: mentransformasi data, membangun matriks similaritas, proses clustering khususnya dalam tesis ini menggunakan metode partisi PAM dan mengekstrak bicluster. Algoritma similarity based biclustering-PAM clustering dapat mengetahui ekspresi regulasi dari tiap bicluster pada tiga dataset yaitu: Diabetes Melitus tipe II, Diabetes Retinopati, dan Limfoma. Akurasi yang diperoleh dari algoritma Similarity Based Biclustering untuk masing-masing dataset yaitu Diabetes Melitus tipe II sebesar 0.55, Diabetes Retinopati sebesar 0.80 dan Limfoma sebesar 0.83.

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In this research implements Similarity Based Biclustering algorithm by using PAM Clustering method in three dataset of microarray gene expression. Aim of this research is to know the regulated expression of each obtained bicluster and to know the performance of Similarity Based Biclustering PAM Clustering algorithm based on the result of group condition analysis. Similarity Based Biclustering is theoretically composed of four main stages transforming data, constructing matrix similarity, clustering process, especially in this thesis using PAM partition algorithm and extracting bicluster. Similarity Based Biclustering PAM is able to know the regulatory expression of each bicluster in three datasets Diabetes Mellitus type 2, Diabetes Retinopathy, and Lymphoma. Accuracy obtained from Similarity Based Biclustering algorithm for each dataset is 0.55 in data of type 2 diabetes mellitus, 0.80 in diabetic retinopathy data and 0.83 in lymphoma data."

"Telemedicine merupakan solusi ideal untuk menjadi layanan kesehatan di era COVID-19. Halodoc merupakan salah satu aplikasi telemedicine terbaik di Indonesia. Sejak tahun 2022, Halodoc sudah mempunyai lebih dari 15.000.000 pengguna sehingga perlu mengganti fokus bisnisnya dari product oriented menjadi customer oriented. Halodoc perlu melakukan analisis customer segmentation untuk mengetahui karakteristik pengguna lebih dalam. Analisis ini menggunakan salah satu teknik data mining yaitu clustering menggunakan algoritma K-Prototypes. Atribut penggunaan voucher, total transaksi, kategori produk, spesialis dokter, provider asuransi, kelompok usia, merek handphone, dan lokasi digunakan pada penelitian ini. Pengguna Halodoc yang melakukan transaksi minimal 1 kali selama November 2021 hingga Januari 2022 yang berjumlah 193.000 pengguna akan disegmentasi. Hasilnya adalah pengguna Halodoc dapat disegmentasi menjadi 4 status sosial yaitu working class, petty bourgeoise, middle class, dan high class. Status sosial yang memiliki ukuran terbesar adalah middle class yaitu dengan proporsi 46,69% dari keseluruhan pengguna. Pengguna yang paling potensial untuk Halodoc adalah yang berasal dari status sosial High Class karena memiliki frekuensi transaksi terbanyak dan nominal pengeluaran terbesar.

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Telemedicine is the ideal solution to become a healthcare service in COVID-19 era. Halodoc is one of the best telemedicine applications in Indonesia. Since 2022, Halodoc has more than 15.000.000 users, so they need to change its business focus from product oriented to customer oriented. Halodoc needs to do customer segmentation analysis to find out more about user’s characteristics. This analysis uses one of data mining techniques which is K-Prototypes Clustering. Voucher usage, total transaction, doctor specialist, insurance provider, age group, mobile phones’s brand, and location are used in this study. Halodoc’s users who make transactions at least 1 time during November 2021 to January total 193.000 users will be segmented. The results is Halodoc’s users can be segmented into 4 social classes such as working class, petty bourgeoise, middle class, and high class. Social status that has the largest size is the middle class with the proportion of 46.69% of the total users. The most potential users for Halodoc are those from High Class social status because they have the highest transaction frequency and the largest nominal spending."