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Formosa Journal of Computer and Information Science
Published by PT FORMOSA CENDEKIA GLOBAL
ISSN : -     EISSN : 28303040     DOI : https://doi.org/10.55927/fjcis.v1i2.1151
Core Subject : Science,
Computer Science & IT
Formosa Journal of Computer and Information Science (FJCIS) is an international platform for scientists, academics, practitioners and engineers involved in all aspects of computer science and information sciences to publish high quality, up todate, peer review papers. It is an international research journal sponsored by Formosa Publisher. The journal provide a platform for survey, research and review articles from experts in the field, promoting insight and understanding of the state of the art, and trends in computer and information sciences. The contents include original research and innovative theory and applications from all parts of the world. The journal publish articles twice in a year (March and August).
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 63 Documents
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Analysis of PT PLN (Persero)'s New Installation Waiting List Using the K-Means Clustering Algorithm Ernawati Ernawati; Dewi Agushinta R
Formosa Journal of Computer and Information Science Vol. 5 No. 1 (2026): March 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjcis.v5i1.16429

Abstract

This study examines the application of the K-means clustering algorithm to analyze new installation waiting list data obtained from the last three months of 2024. Only entries categorized under new installation requests were selected as the primary dataset. The analysis began by determining the optimal number of clusters: a high volume of new installation waiting lists (C1), a medium volume (C2), and a low volume (C3). Data mining processes were carried out using the RapidMiner tool, producing the following results: 6 UIDs/UIWs were classified into the high cluster (C1), 7 into the medium cluster (C2), and 9 into the low cluster (C3). The clustering performance was subsequently validated using the Davies–Bouldin Index, yielding a final score of 0.486, consistent with the RapidMiner output.
Interpretive Practices of PLC Based Automation in Industrial Production Systems under Dynamic Operational Conditions Ferdianto Tangdililing; Stefany Yunita Baralangi
Formosa Journal of Computer and Information Science Vol. 5 No. 1 (2026): March 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjcis.v5i1.16489

Abstract

This study examines interpretive practices in PLC-based automation, particularly how real-time data supports adaptive decision-making in PLC–SCADA manufacturing systems. Using a mixed-method approach, the findings reveal that system effectiveness depends not only on deterministic control logic but also on the integration of sensor data, HMI visualization, and adaptive control. Under dynamic conditions, effective data interpretation enhances production efficiency, reduces downtime, and accelerates response to disruptions. Furthermore, the integration of PLCs with IoT and data analytics improves system flexibility and reduces decision ambiguity. The study concludes that successful PLC automation relies on the synergy between control technology, data interpretation, and human–machine interaction, contributing to the development of more adaptive and intelligent production systems.
Comparative Analysis of Traditional Machine Learning and Sequential Deep Learning Models for Spam Email Classification Harliana Harliana; Hartatik Hartatik; Achmad Alvi Yudanuari
Formosa Journal of Computer and Information Science Vol. 5 No. 1 (2026): March 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjcis.v5i1.16502

Abstract

This study compares the performance of traditional machine learning methods and sequential deep learning models for text-based spam classification. The primary issue addressed is the lack of consistent, fair evaluation across these approaches due to variations in datasets, preprocessing techniques, and experimental settings across previous studies. To overcome this limitation, this research proposes a controlled comparative evaluation framework by employing a unified dataset, standardized preprocessing procedures, consistent data splitting, and identical evaluation metrics. The dataset used consists of 5,572 messages with an imbalanced class distribution; therefore, oversampling was applied to the training data to mitigate bias. The evaluated models include TF-IDF-based Logistic Regression as the baseline, as well as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs) as deep learning models.
Building an Annotated Corpus of Advice-Giving in Indonesian Thesis Supervision for Educational Text Mining Elok Putri Nimasari; Adi Fajaryanto Cobantoro; Mohammad Bhanu Setyawan; Ismail Abdurrozaq; Ariyanti Ariyanti; Navila Uliya Sahidah
Formosa Journal of Computer and Information Science Vol. 5 No. 1 (2026): March 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjcis.v5i1.16529

Abstract

While educational text mining has widely examined student feedback and institutional evaluation, little attention has been paid to advice-giving in thesis supervision as an interactional and power-relational practice. Therefore, this present study aims to analyze and build a domain-sensitive annotated corpus of advice-giving in Indonesian thesis supervision for future educational text mining. Using a qualitative-informed corpus development research design, the study collected and analyzed 155 annotated utterances drawn from authentic thesis supervision transcripts across Indonesian universities. The results identified six advice-giving labels classified into three interactional modes: power-over, power-gaining, and power-maintaining following Zhang and Hyland’s theoretical of power and roles. Cohen’s Kappa reached 1.00, indicating perfect annotation agreement. The corpus contributes a reliable methodological foundation for AI-assisted analysis of supervisory discourse and inclusive academic supervisory.
Enhancing Business Model Validation Using Artificial Intelligence: Insights from Student Business Model Canvas Analysis Dimas Setiawan; Ridho Pamungkas; Mei Lenawati; Noordin Asnawi
Formosa Journal of Computer and Information Science Vol. 5 No. 1 (2026): March 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjcis.v5i1.16564

Abstract

This study aims to identify common weaknesses in students’ Business Model Canvas (BMC) and examine the role of Artificial Intelligence (AI) in improving early-stage business validation. A qualitative descriptive approach was employed using aggregated and anonymized data from 30 student business models. Data were analyzed using thematic coding to identify recurring patterns. The findings reveal that 70% of students struggle with unclear value propositions, 63% define overly broad customer segments, and 57% lack structured revenue models. AI-assisted analysis improves clarity, focus, and logical consistency of business models. This study proposes an AI-BMC conceptual framework as a decision-support approach for entrepreneurship learning. The findings contribute to bridging intuitive business ideation with AI-assisted validation.
IoT-Based Multi-Sensor Fusion for Goat Behavioral Pattern Recognition Using K-Means Clustering in a Smart Farming Environment Yudhistira Pratama; Normalina Napitupulu; Zulhamsyah Fachrurrazi Nasution; Adli Abdillah Nababan
Formosa Journal of Computer and Information Science Vol. 5 No. 1 (2026): March 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjcis.v5i1.16599

Abstract

Monitoring goat behavior in commercial farms typically relies on direct observation, which does not scale and misses conditions that develop gradually. This study deployed an eight-sensor IoT network across two zones of a slatted-floor goat pen in North Sumatra, Indonesia, and applied K-Means clustering to 49 days of sensor data. After a systematic data cleaning step that removed sensor dropouts, ADC saturation events, and an isolated methane spike, 213,704 records were retained (98.6% of raw data). K-Means with K=8 on the cleaned dataset yielded a Silhouette Score of 0.297 and Davies-Bouldin Index of 1.177, identifying eight behavioral and environmental states without a dedicated anomaly cluster. Results include two heat stress levels (THI means 90.7 and 92.1), three nocturnal resting states differentiated by waste pit gas concentration, a daytime active-vocal state, and an evening post-feeding fermentation peak.
Implementation of Decision Support System with Simple Additive Weighting (SAW) Method for Determination of Social Assistance Recipients: A Case Study in Ciledug, Tangerang Tengku Rafi Syahrial; Meyrson Agintha Sitepu; Satrio Santoso; Ali Mustofa Izzulhaq; Andreya Naufal Subagyo; Ati Zaidiah
Formosa Journal of Computer and Information Science Vol. 5 No. 1 (2026): March 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjcis.v5i1.16608

Abstract

This can lead to dissatisfaction among the community and reduce the effectiveness of social assistance programs. This research aims to develop a Decision Support System (SPK) based on the Simple Additive Weighting (SAW) method to ensure a fairer, more objective, and efficient distribution of social assistance. The SAW method is used to assess aid recipients based on five main criteria, namely family income, number of dependents, home conditions, employment status, and age of the head of the family. Each criterion is weighted according to its level of importance, and the data obtained from the respondents is processed through a process of normalization and final score calculation to determine eligible beneficiaries. The implementation of this method in RT 02, RW 06, Ciledug, Tangerang City, showed a significant increase in the efficiency and accuracy of the selection of social assistance recipients.
Design and Build a Real-Time Based 3 Kg LPG Distribution Information System Using the Laravel Framework at PT. Tasya Gasindo Yasmin Neylanda Pulungan; Meilita Tryana Sembiring; Juliza Hidayati
Formosa Journal of Computer and Information Science Vol. 5 No. 1 (2026): March 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjcis.v5i1.16637

Abstract

PT. Tasya Gasindo, an LPG 3 kg distribution agent in Medan, Indonesia, faced significant operational inefficiencies due to the absence of a real-time stock and demand monitoring system. Trucks frequently returned to the depot with unsold cylinders, causing queuing delays and Pertamina fines for unmet daily Delivery Order targets. This study designed and implemented a web-based distribution information system using the Laravel frame-work (MVC architecture, MySQL) providing real-time stock and demand data across 69 distribution bases. The descriptive-qualitative and software engineering approach included observation, interviews, and documentation. Black Box Testing covered 16 scenarios (all passed) with direct user evaluation. Average monthly residual cylinders decreased from 744 units (2022–2024) to 220 units in March–April 2025 a 70.4% reduction. The Fulfill-ment Rate improved from 99.20% to 99.74%, reducing estimated monthly Pertamina fines from Rp22.32 million to Rp6.60 million
Transient Analysis of EVA Foam Damping and Spatial Optimization of FSR Sensors on Shain Guards Fahrizal Akbar Herbhakti; Africo Ramadhani; Erny Amalia Lestari; Azry Ayu Nabilah; Muhamad Ihsan Hufadz
Formosa Journal of Computer and Information Science Vol. 5 No. 1 (2026): March 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjcis.v5i1.16645

Abstract

Embedding Force Sensitive Resistors (FSR) into EVA foam for smart shin guards is hindered by stiffness mismatch between the soft matrix and rigid sensor. This study determines the optimal embedment depth that balances signal fidelity and structural integrity. Coupled multiphysics FEM simulations (COMSOL) employing a hyper elastic Mooney-Rivlin model and piezoresistive equations were run under a 1500 N peak Gaussian impact pulse. At 2 mm depth, sensitivity reached –85% ΔR but shear stress peaked at a critical 42.5 MPa; 8 mm depth was very safe (12.4 MPa) but gave a weak –25% ΔR. The optimum depth was 5 mm, yielding 24.8 MPa shear stress, –62% sensitivity, 2.5 ms latency, and high SNR. Sensitivity analysis and numerical optimization confirmed this sweet-spot. The computational framework provides precise parameters for manufacturing IoT-enabled smart shin guards.
Predictive Analysis for the Early Detection of Depression in Adolescents Based on Social Media Usage Patterns Genesis Sembiring Depari; Julpan Daniel Simatupang
Formosa Journal of Computer and Information Science Vol. 5 No. 1 (2026): March 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjcis.v5i1.16904

Abstract

This study examines the use of predictive analytics for the early detection of depression among teenagers based on social media usage patterns and behavioral indicators. The research utilizes a secondary dataset consisting of 1,200 adolescent records, including variables such as daily social media usage duration, sleep duration, stress level, anxiety level, addiction tendency, academic performance, physical activity, and depression classification labels. A quantitative approach was applied using machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine. The dataset was processed through data cleaning, encoding, normalization, exploratory data analysis, feature selection, model development, and model evaluation. The results show that sleep duration, daily social media usage, stress level, anxiety level, and academic performance are important predictors of teen depression.

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