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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Bulletin of Information Technology (BIT) Jurnal Ilmiah Multidisiplin Amsir
Indar Kusmanto
Universitas Tomakaka
Religion Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Law, Crime, Criminology & Criminal Justice Public Health Other

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Rancang Bangun Sistem Top-Up Meteran PDAM Berbasis Mikrokontroller Indar Kusmanto; Yuyun; Andani Achmad
Bulletin of Information Technology (BIT) Vol 3 No 3: September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v3i3.314

Abstract

This research aims to build a top-up based PDAM meter tools, which allows users to control water use in their daily needs. This type of research is experimental research where the scope of the problem can be carried out using the literature study method, field data collection methods. The system is made in the form of a prototype. This research produces a product in the form of a tool with a top-up as a payment system. This study uses an RFID sensor as a tool to enter voucher balances into the system. Then arduino uno as a controller of water use through a waterflow sensor and a solenoid valve instead of a faucet to close the water flow. The result of this research is that the device can display information in the form of remaining voucher balances and the amount of water consumption. In this study, water measurement trials have been carried out with an error value of 2.53 percent, and trial charging vouchers worth 20,000 to 100,000, as well as trial use and remaining balance with appropriate results
Android Based Educational Game Application To Introduce Cultureand Tribes In West Sulawesi Muzdalifah, Muzdalifah; Kusmanto , Indar; Hidayat, Hidayat
Jurnal Ilmiah Multidisiplin Amsir Vol 2 No 2 (2024): Juni
Publisher : AhInstitute of Research and Community Service (LP2M) Institute of Social Sciences and Business Andi Sapada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62861/jimat amsir.v2i2.492

Abstract

The aim of this research is to find out how to design and implement an Android-based educational game application to introduce culture and ethnicity in West Sulawesi which is expected to contribute to motivating students to get to know culture and ethnicity in West Sulawesi. This application or system was created using the Java and MySQL programming languages, where this application is used to facilitate learning that is full of modern education by following current technological developments. And based on the results of the implementation, it has been successful and has had an influence on motivation to learn about culture and ethnicity in West Sulawesi
Implementasi Metode Long Short-Term Memory (LSTM) untuk Klasifikasi Berita Online Berdasarkan Konten Teks Kusmanto, Indar; KH, Musliadi; Hidayat, Hidayat; Kristian, Kristian
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.8628

Abstract

This study aims to classify Indonesian-language news using the Long Short-Term Memory (LSTM) method and to evaluate its performance through accuracy, precision, recall, and F1-score metrics. The dataset consists of 48,634 news titles collected from various national and regional portals, covering five main categories: finance, travel, health, food, and sports. The research process involves several text preprocessing stages-tokenization, stop-word removal, normalization, and stemming-followed by feature representation using word embedding and the design of the LSTM model architecture. The model's performance is assessed using a confusion matrix along with additional validation through cross-validation to ensure result consistency. The LSTM model demonstrates strong performance, achieving 90% accuracy, 89% precision, 88% recall, and 89% F1-score, indicating its capability to capture semantic patterns and contextual dependencies in textual data effectively. In addition, LSTM outperforms the baseline method with a 6% increase in accuracy, reinforcing its reliability for Indonesian text classification tasks. Overall, the findings confirm that the combination of optimal preprocessing techniques and a well-designed LSTM architecture enhances the performance of the news classification system and offers significant potential for various text analysis applications in the digital information era.
Klasifikasi Mahasiswa Calon Penerima Beasiswa KIP Menggunakan Algoritma Naive Bayes di Universitas Tomakaka Mamuju Hidayat, Hidayat; KH, Musliadi; Kusmanto, Indar; Kadir, Munawirah; Kristian, Kristian
JURNAL FASILKOM Vol. 15 No. 3 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i3.10784

Abstract

Education is a fundamental aspect of national development that demands equal access to quality education for all. The Smart Indonesia Card (KIP) program is a government initiative aimed at supporting education for underprivileged communities. Tomakaka University, Mamuju, as one of the universities in West Sulawesi, plays an active role in distributing KIP scholarships to students who meet certain criteria. However, the selection process for prospective scholarship recipients has been carried out manually, which may lead to inefficiencies and inaccurate targeting. This study aims to apply the Naïve Bayes algorithm to classify prospective KIP scholarship recipients to make the selection process more objective, fast, and accurate. The research method uses a data mining approach with stages of data preprocessing, dividing training and test data, model training, and testing using the Python programming language on the Google Colab platform. The dataset used is 171 student data, with a division of 75% training data and 25% test data. The test results showed that the Naïve Bayes model achieved an accuracy of 95.35%, with a precision of 97%, a recall of 97%, and a loss of 4.65%, indicating excellent classification performance. Thus, this research contributes to improving administrative efficiency and targeting of KIP scholarship distribution at Tomakaka University, Mamuju.
Co-Authors Andani Achmad Hidayat Hidayat Kristian, Kristian Munawirah, Munawirah Musliadi K H Muzdalifah Muzdalifah Yuyun