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All Journal Jurnal Ilmiah KOMPUTASI Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology
Singgih Jatmiko
Universitas Gunadarma
Computer Science & IT Engineering

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ELECTRONIC VOTING USING DECENTRALIZED SYSTEM BASED ON ETHEREUM BLOCKCHAIN Fajri Fadli; Singgih Jatmiko; Missa Lamsani
Jurnal Ilmiah KOMPUTASI Vol 19, No 1 (2020): Maret
Publisher : STMIK JAKARTA STI&K

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Abstract

Electronic Voting Using Decentralised System Based On Ethereum’s Blockchain is a system made in the form of application used for Electronic Voting. This is done to solve the current problem on conventional electronic voting system where the integrity of the data can’t be ascertain and the result of the vote can be tampered malicious actors. To solve this problem, an electronic voting system will be built that store the data of election results on blockchain to ensure the integrity of the data. The creation of the system involves using the Solidity language, a Turing Complete programming language used on Ethereum. Source code that will be run will first need to be compiled into Bytecode, which will then in turn run on Ethereum Virtual Machine. The finished program will then be decentralised using Blockchain. p { margin-bottom: 0.1in; direction: ltr; line-height: 115%; text-align: left; }p.western { font-family: "Calibri", serif; }p.cjk { font-family: "??"; }p.ctl { }a:link { color: rgb(0, 0, 255); }
Sistem Rekomendasi Buku untuk Perpustakaan Perguruan Tinggi Berbasis Association Rule Laras Dewi Adistia; Tubagus Mohammad Akhriza; Singgih Jatmiko
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 3 No 2 (2019): Agustus 2019
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1168.153 KB) | DOI: 10.29207/resti.v3i2.971

Abstract

One of the services in the university library is an information system to find the availability of library collections and the location of each collection shelf. But not many of these systems provide a mechanism that can recommend visitors not only about the books they want, but also other related books that may be needed. This study uses association rule mining techniques that are applied to library transaction data to identify relationships between books (titles) that attract visitors' attention. Relationships are built on interesting measurements between the titles, namely support and confidence, where support determines the combination of the most frequently borrowed book titles, while confidence produces the possibility that the title of the book will be borrowed along with other books. The pattern of book titles association with high confidence indicates that the titles are very related so it is recommended for visitors to consider borrowing along with the book they are looking for. In addition, the system can also recommend the procurement of new books and rack configurations to improve the visitor's experience when searching for books on the site. In the experiment, the precision of recommendations generated from the system reached 70%. Web applications were developed to help understand the effectiveness of the recommendation system based on association rules.
Assessment Penerapan Manajemen Risiko Teknologi Informasi Menggunakan ISO 31000 Didik Wahyu Setyadi; Singgih Jatmiko
Jurnal Ilmiah KOMPUTASI Vol. 21 No. 2 (2022): Jurnal Ilmiah Komputasi Volume: 21 No. 2, Juni 2022
Publisher : Lembaga Penelitian STMIK Jakarta STI&K

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Unit XYZ merupakan unit pengelola TI pada lembaga pemerintahan yang telah mengintegrasikan ISO 9001:2015, ISO/IEC 20000-1:2018 dan ISO/IEC 27001:2013 ke dalam Integrated Management System (IMS). Sejak tahun 2013 telah menerapkan Risk Management System (RMS) dan telah diperkuat dengan Kebijakan Internal Lembaga tentang Manajemen Risiko yang terintegrasi dengan ISO/IEC 27001:2013. Adapun pengintegrasian RMS dengan ISO 9001:2015 dan ISO/IEC 20000-1:2018 belum ada pedoman khusus yang mengatur hal tersebut. Kondisi ini dapat menyebabkan isu yang menjadi perhatian dalam ISO 9001:2015 dan ISO/IEC 20000-1:2018 tidak terekam dengan baik dalam Risk Register. Untuk mengetahui kesenjangan antara RMS dengan ISO 31000:2018, perlu dilakukan assessment penerapan RMS sesuai standar ISO 31000:2018. Penelitian ini melakukan penilaian terhadap dokumentasi dan implementasi berdasarkan ISO 31000, sehingga diperoleh seberapa besar nilai kesesuaian dan ketidaksesuaian, serta rekomendasi perbaikan terhadap pengelolaan risiko dan peluang yang terintegrasi antara RMS dan IMS. Hasil penelitian terdapat penilaian RMS terhadap proses IMS saat ini adalah 75,93%, terdapat 4 klausul yang belum terpenuhi secara keseluruhan, dan 5 klausul yang hanya terpenuhi sebagian. Hasil penelitian ini dapat digunakan untuk menentukan langkah-langkah perbaikan, serta bisa digunakan untuk penyusunan pedoman manajemen risiko yang terintegrasi antara RMS dengan IMS.
Assessment Penerapan Manajemen Risiko TI Unit XYZ Menggunakan ISO 31000 Didik Wahyu Setyadi; Singgih Jatmiko
Jurnal Ilmiah Komputasi Vol. 21 No. 2 (2022): Jurnal Ilmiah Komputasi Volume: 21 No. 2, Juni 2022
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32409/jikstik.21.2.3061

Abstract

Pustik telah mengintegrasikan ISO 9001:2015, ISO/IEC 20000-1:2018 dan ISO/IEC 27001: 2013 ke dalam Integrated Management System (IMS). Sejak tahun 2013 telah menerapkan Risk Management System (RMS) dan telah diperkuat dengan Kebijakan Internal Lembaga tentang Manajemen Risiko yang terintegrasi dengan ISO/IEC 27001:2013. Adapun pengintegrasian RMS dengan ISO 9001:2015 dan ISO/IEC 20000-1:2018 belum ada pedoman khusus yang mengatur hal tersebut. Kondisi ini dapat menyebabkan isu yang menjadi perhatian dalam ISO 9001:2015 dan ISO/IEC 20000-1:2018 tidak terekam dengan baik dalam Risk Register . Untuk mengetahui kesenjangan antara RMS dengan ISO 31000:2018, perlu dilakukan as- sessment penerapan RMS sesuai standar ISO 31000:2018. Penelitian ini melakukan penilaian terhadap dokumentasi dan implementasi berdasarkan ISO 31000, sehingga diperoleh seberapa besar nilai kesesuaian dan ketidaksesuaian, serta rekomendasi perbaikan terhadap pengelo- laan risiko dan peluang yang terintegrasi antara RMS dan IMS. Hasil penelitian terdapat penilaian RMS terhadap proses IMS saat ini adalah 75,93%, terdapat 4 klausul yang belum terpenuhi secara keseluruhan, dan 5 klausul yang hanya terpenuhi sebagian. Hasil peneli- tian ini dapat digunakan untuk menentukan langkah-langkah perbaikan, serta bisa digunakan untuk penyusunan pedoman manajemen risiko yang terintegrasi antara RMS dengan IMS
Development of an Indonesian Hoax Detection System Using Logistic Regression Based on TF-IDF Dewa Samudra Anggeng Suryama; Singgih Jatmiko
J-INTECH ( Journal of Information and Technology) Vol 13 No 02 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i02.2127

Abstract

The massive spread of fake news (hoaxes) on digital platforms has become a serious challenge in Indonesia, with the potential to disrupt social stability and undermine public trust. This background drives the urgency of developing an automated system to combat disinformation. Unlike previous works relying on deep learning with high computational cost, this study demonstrates that a lightweight approach remains highly effective for Indonesian hoax detection. This study aims to develop and evaluate a lightweight and effective automatic classification system to detect Indonesian-language hoaxes using a machine learning approach. The method used is Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction to represent text content numerically, which is then classified using the Logistic Regression algorithm. This approach was chosen for its computational efficiency and ease of interpretation. The study utilizes a dataset collected from verified sources, consisting of 7,075 Indonesian-language news articles, which were divided into 80% training data and 20% test data. The evaluation results on the test data show excellent model performance, achieving an accuracy of 94.98%, a precision of 0.95, and an average F1-Score of 0.95. Specifically, the model demonstrated a strong ability to identify hoaxes with a recall value of 98% for the hoax class. This study concludes that the combination of TF-IDF and Logistic Regression is an efficient and accurate approach for Indonesian hoax detection, offering a practical solution that can be further developed to combat disinformation.
Co-Authors Abd. Rasyid Syamsuri Adistia, Laras Dewi Dewa Samudra Anggeng Suryama Didik Wahyu Setyadi Fajri Fadli Missa Lamsani Tubagus Mohammad Akhriza