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"Laporan magang ini disusun karena penulis menyadari adanya risiko input error pada pekerjaan data entry contract assessment yang dilakukan secara manual oleh KAP LIN pada proyek dengan PT RED. Pada laporan ini, akan dibahas evaluasi terhadap aktivitas kontrol berupa data entry control dan independent check yang dilakukan untuk meminimalisir risiko input error tersebut. Metode evaluasi yang dilakukan yaitu dengan cara memperbandingkan antara pelaksanaan kontrol yang dilakukan KAP LIN saat ini dengan rekomendasi yang terdapat pada teori data entry control dan independent check yang digunakan oleh penulis pada laporan ini. Hasil evaluasi menunjukkan bahwa aktivitas kontrol yang dilakukan KAP LIN pada proyek ini masih kurang memadai, karena kontrol yang ada belum dilakukan secara otomatis, pelaksanaan independent check yang belum rutin, dan sosialisasi hasil independent check yang belum menyeluruh kepada seluruh anggota tim yang melakukan pekerjaan data entry.

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This internship report was prepared because the author is aware of the risk of input errors in the data entry contract assessment work which is carried out manually by KAP LIN on their project with PT RED. This internship report will discuss about the evaluation of control activities in the form of data entry controls and independent checks that were carried out to help minimize the risk of input error. The evaluation method used is by comparing the implementation of controls carried out by KAP LIN at this time, with the recommendations contained in the theory of data entry controls and independent check used by the author in this report. The evaluation shows that the control activities carried out by KAP LIN in this project are still inadequate, because the existing controls have not been carried out automatically, the implementation of independent checks has not been done routinely, and the socialization of the results of independent checks has not been fully conveyed to all team members who did the data entry work."

"Saat ini repositori dan depositori LIPI sedang memfokuskan pada kegiatan pengolahan data ilmiah yang dihasilkan dari suatu penelitian. Mengingat betapa pentingnya data penelitian maka dalam proses pengelolaannya diperlukan sebuah data management plan. Salah satu proses data management plan yaitu kurasi data. Proses kurasi data diperlukan untuk menjamin data yang dipublikasikan lebih akurat, informatif, dan berkualitas sehingga berdampak pada peningkatan akses data. Tujuan penelitian adalah menentukan proses/alur data management untuk proses kurasi data yang tepat untuk diterapkan di Perpustakaan penelitian (PDII LIPI) guna memastikan data sesuai aturan yang berlaku (valid), informatif, dan berkualitas agar data dapat digunakan kembali (reuse) dan menghasilkan data penelitian baru (reproduce). Metode penelitian menggunakan metode kualitatif dimana data diambil dengan teknik wawancara mendalam (in-depth interview) semi terstruktur. Responden penelitian adalah CIFOR, P2O LIPI, BMKG, BPS, dan Kemkes. Hasil yang diperoleh adalah responden sudah menerapkan data management termasuk proses kurasi. Alur kurasi antar 5 responden berbeda, tergantung pada kebijakan dan kebutuhan responden. Kesimpulan yang dapat diambil, bahwa tim data management termasuk kurator harus dilibatkan di awal hingga akhir penelitian. Kegiatan kurasi data di perpustakaan penelitian dilaksanakan saling bekerjasama antar peneliti, pustakawan/kurator, dan IT. Tugas utama kurasi data penelitian yang dilakukan pustakawan/kurator adalah menentukan dan memastikan kualitas data penelitian dengan tujuan agar data dapat diakses dengan mudah dan data dapat dimanfaatkan kembali."

"ABSTRAKDalam dunia militer, khususnya intelejen, pesan suara sering digunakan dalam bertukar informasi jarak jauh. Penyadapan lumrah terjadi meski pesan terenkripsi. Divais berfitur keamanan tinggi yang beredar masih langka dan mahal. Kasus ini penulis tujukan pada penggunaan HT atau Handie Talkie. Topik ini menciptakan solusi bernama VoCABS atau Voice Crypto Alignment Block Synchronization, berupa algoritma sinkronisasi sinyal (pilot) untuk mengatasi misalignment-block yang merupakan ketidakpresisian divais menyinkronisasi sinyal suara, meleset sepersekian detik saja akan ada informasi yang hilang. Algoritma mendeteksi pola dan mencocokan nilai sampling, sehingga ditemukan nilai tertinggi sebagai acuan. Penulis berfokus memastikan supaya tanda mulai dan berhenti pada divais bisa sama (sinkron). Pengembangan berikutnya diharapkan algoritma diimplentasikan ke alat yang terhubung dengan HT, bila divais saling konfirmasi telah sinkron, percakapan HT bisa dimulai. Skenario menggunakan 3 HT berbeda merk. Di ruang tertutup, HT berpasangan berjarak 1 meter diuji mengirim sinyal sinusoidal dengan kondisi buatan dan real, dicocokan pada sinyal sample di posisi penerima. HT dihubungkan ke laptop (pengganti orang) dengan bantuan Audacity. Berlandaskan metode Matlab Xcorrelation algoritma ini berhasil mendeteksi gelombang, delay, lag difference, index fungsi, dan nilai sampling secara presisi sehingga pola sinyal pengirim dan penerima bisa sinkron dengan rentang akurasi 98 hingga 100%.

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ABSTRACT

n the military world, especially intelligence, voice messages are often used in exchanging information over long distances. Tapping is common even if the message is encrypted. The outstanding high security features are still rare and expensive. This case the author focuses on the use of HT or Handie Talkie. This topic creates a solution called VoCABS or Voice Crypto Alignment Block Synchronization, in the form of a signal synchronization algorithm (pilot) to overcome misalignment block which is an imperfection of devices synchronizing sound signals, missing only a split second there will be missing information. The algorithm detects the pattern and matches the sampling value, so that the highest value is found as a reference. The author focuses on ensuring that the start and stop signs on the device can be the same (synchronous). The next development is expected that the algorithm will be implemented to devices that are connected to HT, if the devices for mutual confirmation are synchronized, HT conversations can be started. The scenario uses 3 different HT brands. In a closed room, a pair of 1 meter HT is tested sending a sinusoidal signal with artificial and real conditions, matched to the sample signal at the receiver's position. HT is connected to a laptop (substitute for person) with the help of Audacity. Based on Matlab Xcorrelation method, this algorithm is able to detect waves, delay, lag difference, function index, and sampling values ​​precisely so that the sender and receiver signal patterns can be synchronized with accuracy range of 98 to 100%."

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ABSTRACTDoing data science is difficult. Projects are typically very dynamic with requirements that change as data understanding grows. The data itself arrives piecemeal, is added to, replaced, contains undiscovered flaws and comes from a variety of sources. Teams also have mixed skill sets and tooling is often limited. Despite these disruptions, a data science team must get off the ground fast and begin demonstrating value with traceable, tested work products. This is when you need Guerrilla Analytics. n this book, you will learn about:The Guerrilla Analytics Principles: simple rules of thumb for maintaining data provenance across the entire analytics life cycle from data extraction, through analysis to reporting.Reproducible, traceable analytics: how to design and implement work products that are reproducible, testable and stand up to external scrutiny.Practice tips and war stories 90 practice tips and 16 war stories based on real-world project challenges encountered in consulting, pre-sales and research.Preparing for battle: how to set up your team's analytics environment in terms of tooling, skill sets, workflows and conventions.Data gymnastics: over a dozen analytics patterns that your team will encounter again and again in projectsThe Guerrilla Analytics Principles: simple rules of thumb for maintaining data provenance across the entire analytics life cycle from data extraction, through analysis to reportingReproducible, traceable analytics: how to design and implement work products that are reproducible, testable and stand up to external scrutinyPractice tips and war stories: 90 practice tips and 16 war stories based on real-world project challenges encountered in consulting, pre-sales and researchPreparing for battle: how to set up your team's analytics environment in terms of tooling, skill sets, workflows and conventionsData gymnastics: over a dozen analytics patterns that your team will encounter again and again in projects."

"Doing data science is difficult. Projects are typically very dynamic with requirements that change as data understanding grows. The data itself arrives piecemeal, is added to, replaced, contains undiscovered flaws and comes from a variety of sources. Teams also have mixed skill sets and tooling is often limited. Despite these disruptions, a data science team must get off the ground fast and begin demonstrating value with traceable, tested work products. This is when you need Guerrilla analytics.In this book, you will learn about :The Guerrilla analytics principles, simple rules of thumb for maintaining data provenance across the entire analytics life cycle from data extraction, through analysis to reporting.Reproducible, traceable analytics, how to design and implement work products that are reproducible, testable and stand up to external scrutiny.Practice tips and war stories, 90 practice tips and 16 war stories based on real-world project challenges encountered in consulting, pre-sales and research.Preparing for battle: how to set up your team's analytics environment in terms of tooling, skill sets, workflows and conventions.Data gymnastics, over a dozen analytics patterns that your team will encounter again and again in projects."