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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Ilmiah Multidisiplin Amsir
Kusmanto, Indar
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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 Informasi Toilet Kosong Berbasis Arduino Nasruddin; Kusmanto, Indar
Jurnal Ilmiah Multidisiplin Amsir Vol 2 No 1 (2023): Desember
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.v2i1.297

Abstract

Designing an Arduino-Based Empty Toilet Information Prototype, the electronic components needed are not many, however, the Arduino language, namely C++, a derivative of the C language, is complicated because you have to relearn its functions and how to write them, which is different from Java or Delphi. Implementation of Arduino-based Empty Toilet Information Design is carried out by simulating the process of using paid public toilets and can also be used as a Portable Toilet for other activities such as natural disasters, concerts or other mass activities. How to analyze the design and construction of toilet information, Arduino is needed as a microcontroller which receives input from sensors by writing the C++ program language to give commands to the sensors so that they become input and are executed by Arduino.
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 Hidayat Hidayat Kristian, Kristian Munawirah, Munawirah Musliadi K H Nasruddin