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All Journal CommIT (Communication & Information Technology) International Journal of Advances in Data and Information Systems Jurnal Teknik Informatika (JUTIF) International Journal of Electronics and Communications Systems Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) JGGAG (Journal of Games, Game Art, and Gamification)
Sani Muhamad Isa, Sani Muhamad
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Arts Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Other

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 1 
Digital Technology: the Effect of Connected World to Computer Ethic and Family Soewito, Benfano; Isa, Sani Muhamad
CommIT (Communication and Information Technology) Journal Vol 9, No 1 (2015): CommIT Vol. 9 No. 1 Tahun 2015
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v9i1.1654

Abstract

The development of digital technology such as smartphones, tablets and other gadgets grows very rapidly in the last decade so does the development of mobile applications for those mobile systems or smartphones. Unfortunately, those applications often do not specify the age range for their users. This is actually a problem in the world of digital technology and software development. It is not yet known whether the applications is good be used for children or not. Nowadays, parents are faced with the dilemma of allowing their children to use these modern gadgets, which often lead to serious addiction or keeping them in the dark and risk raising ignorant kids. This research shows 80% of respondents agree or strongly agree that the gadget will affect to the development of children social skill. Therefore, in this research, the framework for ethical assessment is introduced and it can be applied to digital technology included gadget and its application in order to mitigate the negative effect of digital technology and gadgets.
Predictive Maintenance Air Conditioner Using Machine Learning Fambudi, Ranggi Tino; Isa, Sani Muhamad
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 3 (2024): Edisi Juli
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i3.414

Abstract

Predictive maintenance will take care of the machine's needs in terms of power loss from damage that lowers performance, operational costs from severe damage, business interruptions from damage that renders the machine unusable, and much more. Almost every home has an air conditioner, the machine that requires constant maintenance of temperature and humidity, especially in offices with servers or control rooms. Preventive and predictive maintenance is necessary to identify the necessary steps for technicians to take when handling an AC before the damage worsens. In this research we implemented and proposed an Air Conditioner detection system using machine learning with three methods, namely K-Nearest Neighbor, Decision Tree, and Random Forest. In order to understand the actual conditions of each AC, we use data sheets that we gathered through surveys with engineering teams at multiple hotels as well as technical teams that handle servers and control rooms. There are 20 features in the gathered data set; however, since only 14 of the features affect the value, extraneous data will be removed. Then the data was divided into two groups, namely 23 AC Failures yes, which means the AC condition is not normal and 110 AC Failures No, which means the AC condition is not damaged. Using the stratified random sample method, 25% of the data will be oversampled. In this study, Kbest and backward elimination were employed for feature selection. The SMOTE approach was then applied for oversampling due to the unbalanced groups. With accuracy values of 91.18%, precision 91.18%, recall 90.90%, and f1-score 90.92%, the Random Forest model with the suggested model outperformed the Decision Tree and KNN models, according to the experimental findings.
Data Mining untuk Memprediksi Animo Masyarakat terhadap Proses Penerimaan Peserta Didik Baru Berek, Anggelina Bete; Isa, Sani Muhamad
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 6, No 1 (2025): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v6i1.563

Abstract

Data Mining techniques can now be implemented in all aspects of society because of development in technology. Data Mining can be used in education. There is the possibility of being employed for educational purposes to predict achievements. The involvement of the community is one of the factors that influence the quantity of students at the school. The research project is using Data Mining to predict the factors that impact social engagement. The demographic information used in this study came from the parents of potential candidates. The techniques used are Decision tree, Naïve bayes, and Support Vector Machine. Their accuracy scores are evaluated by a confusion matrix. The results of this study are below: Decision tree 80.16%, Naïve Baye 79.94%, and Support Vector Machine 86.02%.  Based on the comparison results, it can be concluded that the highest accuracy is achieved by using the Support Vector Machine algorithm, while the factor that affects public sentiment is ayah penghasilan.
Comparison of Transfer Learning Using VGG16, MobileNetV2, and ResNet50 for Pornography Image Detection SihWardana, Christopher Ade; Isa, Sani Muhamad
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1418

Abstract

The rapid growth of digital technology is vital for the Indonesian Scout to reach and interact with its members. The National Indonesian Scout (Kwarnas) uses the “Ayo Pramuka” social media application to support this. However, such platforms risk exposing users, especially teenagers, to harmful content like pornography. This research applies Computer Vision and Transfer Learning Convolutional Neural Networks (CNNs) to detect pornographic images automatically. The objective is to identify the CNN model (VGG16, MobileNet V2, ResNet 50) with the highest detection accuracy and determine the impact of color space preprocessing. The method includes two stages first, image preprocessing by converting RGB images to HSV and YCbCr second, feature extraction using pre-trained CNNs with freezing and fine-tuning. A dataset of 4060 images was used for training and testing. Without preprocessing, VGG16 achieved the best accuracy of 99.01%. When RGB images were converted to HSV, ResNet 50 produced the highest accuracy of 99.51%. The findings show that combining color space transformation and Transfer Learning CNN significantly improves pornographic content detection in the “Ayo Pramuka” Application, enhancing safe digital engagement for Indonesian Scouts.
Efficient ECG-Based Sleep Apnea Detection Using CNN-GRU and Sparse Autoencoder Putra, Ramadhian Eka; Isa, Sani Muhamad
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5182

Abstract

Sleep apnea is a serious and common breathing disorder that occurs during sleep, characterized by repeated pauses in breathing that can increase the risk of hypertension, heart disease, and stroke. Early detection of sleep apnea is crucial, but conventional methods, such as polysomnography, are expensive, complex, and inefficient for mass screening. Therefore, an automated system based on physiological signals such as an electrocardiogram (ECG) is needed for a more practical and efficient approach. This study proposes a sleep apnea classification model utilizing a combination of 1D Convolutional Sparse Autoencoder (1DCSAE), Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU) architectures, referred to as the SAE-DEEP model. This method is designed to automatically extract features while minimizing the need for preprocessing. Four testing scenarios were conducted to evaluate the impact of signal reconstruction and preprocessing on classification performance. Experimental results show that the CNN-GRU model with signal reconstruction using 1DCSAE achieves an accuracy of 89.8%, a sensitivity of 90.1%, and a specificity of 89.2%, demonstrating balanced and stable classification performance. Additionally, this model was proven to work effectively without complex preprocessing steps, making it a potential solution for efficient sleep apnea detection systems. These findings could contribute to the development of more straightforward, reliable, and clinically viable ECG-based classification systems, as well as wearable devices. In doing so, the proposed model addresses a critical gap in sleep apnea screening, underscoring the urgent need for accessible and cost-effective diagnostic tools. 
Deep Representation Learning for Institutional Risk Ranking Under Label Scarcity Irawan, Dedy; Isa, Sani Muhamad
International Journal of Electronics and Communications Systems Vol. 6 No. 1 (2026): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v6i1.31069

Abstract

Large-scale administrative reporting systems generate complex multivariate signals, yet institutional risk identification remains challenging when verified labels are scarce. This study develops a deep representation learning framework for anomaly detection and institutional risk ranking under label-scarce conditions. The framework integrates a Variational Autoencoder (VAE) for unsupervised anomaly detection with a Hybrid Autoencoder and risk prediction head using Positive-Unlabeled learning, while XGBoost serves as a supervised benchmark. The models were evaluated on multivariate institutional signals from the Indonesian Higher Education Database using stratified cross-validation and ranking-based metrics. The VAE consistently assigned greater reconstruction deviations to institutions with verified violations, demonstrating its ability to identify anomalous multivariate patterns without relying on complete labels. The Hybrid Autoencoder produced strong risk-ranking performance that closely approached the supervised benchmark while preserving uncertainty among unlabeled institutions rather than treating them as negative cases. These findings demonstrate that combining deep representation learning with Positive-Unlabeled learning provides a practical approach to anomaly detection and risk prioritization when verified labels are severely limited. The proposed framework offers an adaptable foundation for signal-based early warning systems in large-scale distributed administrative reporting environments characterized by multivariate data and label scarcity
Improving English Learning by Gamification with MDA Framework Angelia, Frieska; Suharjito, Suharjito; Isa, Sani Muhamad
Journal of Games, Game Art, and Gamification Vol. 5 No. 2 (2020)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/jggag.v5i2.7474

Abstract

Information technology is proliferating and drives the learning process toward e-learning. At the moment, people can learn from their gadget through learning sites, applications, or games. However, to improve users’ motivation, e-learning must be appealing. Researchers have introduced the concept of gamification to make learning more entertaining. The primary purpose of this research is to understand how the proposed gamification elements using Mechanics, Dynamics, and Aesthetics (MDA) framework improve users' motivation and learning outcomes in English learning. For this research, we use 6-11 framework defining six aspects of emotion and eleven aspects of instinct. We collected users’ scores then analyzed them by using a t-test. We also used a questionnaire to access the users' experience. The results suggest that most of the respondents are motivated to learn more deeply. We conclude that the proposed gamification elements improve users' motivation and learning outcomes.
Co-Authors Angelia, Frieska Benfano Soewito Berek, Anggelina Bete Dedy Irawan Fambudi, Ranggi Tino Putra, Ramadhian Eka SihWardana, Christopher Ade Suharjito Suharjito, Suharjito