cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Andri Syafrianto
Contact Email
andrisyafrianto@gmail.com
Phone
+628127828138
Journal Mail Official
lp2m@stmikelrahma.ac.id
Editorial Address
Jl. Sisingamangaraja No. 76 Mergangsan, Yogyakarta
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen
Published by LP2M STMIK El Rahma Yogyakarta
ISSN : 16932277     EISSN : 27152944     DOI : https://doi.org/10.61805
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering
Jurnal FAHMA adalah jurnal yang memuat naskah ilmiah dari peneliti, akademisi, maupun praktisi, berupa hasil penelitian, tinjauan pustaka ( literature review ) dan/atau bentuk karya tulis ilmiah lainnya, yang khusus mengkaji bidang Ilmu Komputer antara lain sebagai berikut : Kecerdasan Buatan, Pembelajaran Mesin, Penambangan Data, Sistem Pakar, Sistem Pendukung Keputusan, Pemrograman Web, Komputasi Bergerak, Jaringan Komputer, Sistem Informasi, Sistem Basis Data, Sistem Keamanan, Strategi Bisnis, Ánalisis Bisnis, Bisnis Digital, Etika Bisnis, Model Bisnis, Strategi Manajemen, Manajemen Proses Bisnis, Manajemen Hubungan Pelanggan, Aplikasi Enterprise, Pemasaran Digital
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 13 Documents
 1   2 
Search results for , issue "vol 24 no 2 (2026): mei 2026" : 13 Documents clear
Analisis Sentimen Berbasis Aspek Pada Ulasan Produk Fashion Shopee  Dengan Normalisasi Bahasa Slang Herdiesel Santoso; Wahyuni Nareswari
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 2 (2026): Mei 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i2.201

Abstract

The rapid growth of e-commerce in Indonesia, particularly within Shopee’s fashion category, has generated a large volume of customer reviews that can serve as valuable sources of business insights. However, sentiment classification of these reviews is challenged by the extensive use of informal slang expressions and imbalanced sentiment distributions. This study develops an Aspect-Based Sentiment Analysis (ABSA) model by comparing the performance of Multinomial Naïve Bayes (MNB) and Support Vector Machine (SVM), while integrating slang normalization and the Synthetic Minority Oversampling Technique (SMOTE). A dataset of 5,877 customer reviews was analyzed using an 80:20 train–test split and evaluated through a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results indicate that, without preprocessing, SVM achieved an accuracy of 0.852, outperforming MNB with an accuracy of 0.722. After applying slang normalization and SMOTE, the performance of both models improved substantially. MNB achieved an accuracy of 0.853, while SVM attained the highest performance with an accuracy of 0.938, precision of 0.939, recall of 0.938, and F1-score of 0.937. These findings demonstrate that integrating slang normalization and SMOTE effectively enhances aspect-based sentiment classification, with SVM providing the best performance on Shopee fashion product reviews.
Optimasi K-Means++ Menggunakan Principal Component Analysis (PCA) pada Klasterisasi Profil Kelulusan Mahasiswa Herdiesel Santoso; Hana Solikatun
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 2 (2026): Mei 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i2.202

Abstract

Timely graduation is a key indicator of student success and institutional effectiveness in higher education. However, clustering student academic records containing mixed data types (numerical and categorical) using the conventional K-Means algorithm often leads to distance bias and reduced clustering quality due to the curse of dimensionality. This study proposes an optimized K-Means++ approach integrated with One-Hot Encoding and Principal Component Analysis (PCA) to improve clustering performance. The model was evaluated using 200 graduate records from STMIK El Rahma Yogyakarta. The results show that reducing the dataset to two principal components significantly enhances cluster quality. Validation metrics indicate that the Silhouette Score increased from 0.3275 to 0.4979, the Davies–Bouldin Index decreased from 1.405 to 0.871, and the Calinski–Harabasz Index improved from 70.448 to 168.035. The optimized model identified two distinct groups: Academically Stable Students (157 students) and At-Risk Working Students (43 students), the latter predominantly consisting of part-time employed students. These findings provide valuable insights for developing data-driven Academic Early Warning Systems (EWS) that enable higher education institutions to identify students at risk of delayed graduation and implement targeted intervention strategies.
Analisis Active learning SVM berbasis Margin Sampling pada Sentimen YouTube MBG Minarwati; Alvian Putra Hardiadi
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 2 (2026): Mei 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i2.203

Abstract

The Free Nutritious Meal Program (MBG) has generated extensive public discussion on YouTube, making sentiment analysis a valuable tool for understanding public perceptions. This study investigates the effectiveness of Support Vector Machine (SVM)-based Active Learning (AL) using a Margin Sampling strategy and a Human-in-the-Loop (HITL) framework, in which researchers acted as oracles following annotation guidelines for three sentiment classes: Negative, Neutral, and Positive. The dataset comprised 999 labeled Indonesian YouTube comments, split into 599 initial training samples, 200 oracle pool samples, and 200 fixed test samples. Texts were represented using TF-IDF features with unigrams and bigrams (max_features = 5,000). Three approaches were compared: AL-HITL, Simulated Active Learning, and Random Sampling. After 15 iterations involving 50 additional labeled samples, AL-HITL achieved the highest macro F1-score of 0.5389, outperforming Simulated AL (0.4950) and Random Sampling (0.4977). A skip mechanism with a 33.3% skip rate reduced negative learning, whereas the other approaches experienced performance declines from the baseline score of 0.5154. Positive sentiment, the minority class (20%), yielded the lowest F1-score (0.4286), while the majority negative class (40.7%) achieved the highest (0.6098). These findings provide preliminary empirical evidence on the potential and challenges of Margin Sampling-based Active Learning for Indonesian YouTube sentiment analysis.
Mengoptimalkan Header Keamanan pada Website Server OpenLiteSpeed ​​Menggunakan Hardening Berbasis OWASP Sugiyatno Sugiyatno; Untung Subagyo
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 2 (2026): Mei 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i2.204

Abstract

Web service security is essential for maintaining the confidentiality, integrity, and availability of data in modern digital environments. Improperly configured web servers are vulnerable to various security threats, including injection attacks, Cross-Site Scripting (XSS), and brute-force attacks. OpenLiteSpeed is widely adopted due to its high performance; however, its default configuration may still expose security vulnerabilities. This study aims to enhance the security of OpenLiteSpeed web services running on Ubuntu Server through the implementation of OWASP-based hardening techniques, with a particular focus on security header optimization. The novelty of this research lies in the implementation of customized security header configurations, including Content Security Policy (CSP), X-Frame-Options, and HTTP Strict Transport Security (HSTS), tailored to application requirements. An experimental approach was employed, involving vulnerability assessment, firewall configuration, SSL/TLS implementation, permission management, security header optimization, and comparative security testing using OWASP ZAP before and after hardening. The results demonstrate a significant reduction in both the number and severity of vulnerabilities, confirming the effectiveness of OWASP-based hardening in strengthening Linux-based web server security.
Pengembangan Smart Lighting Berbasis ESP32 dan Blynk untuk Mendukung Smart Village Wahju Tjahjo Saputro; Hamid Muhammad Jumasa; Murhadi
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 2 (2026): Mei 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i2.205

Abstract

Efficient public lighting infrastructure plays a crucial role in supporting the development of smart villages. However, conventional street lighting systems are often energy-intensive and difficult to monitor and control remotely. This study aims to develop an Internet of Things (IoT)-based smart lighting system using the ESP32 WROOM microcontroller and the Blynk platform. The system was developed following the ADDIE framework, which consists of Analysis, Design, Development, Implementation, and Evaluation stages. The hardware components include an ESP32 WROOM DevKitC v4, a PZEM-004T v3 power monitoring module, a 2-channel optocoupler relay, and a smartphone. The software environment comprises Arduino IDE, Fritzing, EasyEDA, and Blynk as the user interface. Experimental results demonstrate that the proposed system can effectively monitor and control public lighting in real time through a smartphone from any location. The system exhibited fast response times, stable operation, and the potential to reduce electricity consumption by 23.33%. These findings indicate that the proposed solution effectively supports modern and energy-efficient smart village infrastructure while providing a low-cost, reliable, and easily deployable smart lighting model.
Audit Teknologi Informasi Untuk Mengoptimalkan Sumber Daya Dan Tata Kelola Teknologi Informasi Siswaya; Rachmad Sanuri
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 2 (2026): Mei 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i2.206

Abstract

This study evaluated the maturity of Information Technology (IT) governance at SMK Koperasi Yogyakarta using the COBIT 2019 framework to optimize the institution's IT resources. The assessment evaluation showed an overall maturity rating of 2.18 (Managed), indicating that although IT processes are functional and documented, they are still lacking standardization across the organization. The analysis identified a significant average gap of 0.82, with the most substantial deficiencies found in operational monitoring and IT unit personnel competency. Therefore, a strategic roadmap is proposed that focuses on policy formalization and staff development to transition the institution towards a more optimal, standardized, and efficient digital environment.
Model Keamanan Data Berbasis Blockchain pada SIG untuk Monitoring Pelayanan Keperawatan Gerontik Edi Iskandar; Sri Setyowati; Edy Prayitno; Parmadi Sigit Purnomo
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 2 (2026): Mei 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i2.207

Abstract

The adoption of Geographic Information Systems (GIS) in healthcare services, particularly location-based gerontological nursing, has increased significantly. However, conventional systems typically rely on centralized architectures that are vulnerable to data tampering and unauthorized access. This study aims to develop and evaluate a blockchain-based data security model integrated with GIS for monitoring gerontological nursing services. A simulation-based approach was employed to design a multi-layer architecture consisting of a GIS layer, blockchain layer, and application layer. The proposed model was evaluated using data integrity, attack detection, and system performance metrics. The results demonstrate that the model achieved 100% data integrity and an average attack detection rate of 99.2%, substantially outperforming conventional systems, which achieved only 70.3%. Although the integration of blockchain introduced additional latency, the performance remained within acceptable operational limits. This study contributes to the advancement of secure GIS–blockchain integration for location-based elderly care services and provides a replicable simulation framework for system evaluation. The findings suggest that the proposed model has strong potential for implementation in homecare services and community healthcare facilities to enhance data security, transparency, and trustworthiness.
Integrasi Kecerdasan Buatan dan Strategi Bisnis dalam Transformasi Digital Maria Atik Sunarti Ekowati; Rousyati; Ahmad Fauzi; Pudji Widodo
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 2 (2026): Mei 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i2.208

Abstract

Digital transformation has become a critical driver of organizational competitiveness in today’s disruptive economic environment. This study examines the integration of Artificial Intelligence (AI) with digital business strategies across the e-commerce, fintech, education, healthcare, and manufacturing sectors. A multi-case quantitative approach was employed using 250 observations collected over a ten-month period from organizational information systems, customer satisfaction surveys, and operational performance indicators. Data analysis was conducted using Random Forest and Support Vector Machine (SVM) algorithms to predict business strategy effectiveness, while multiple regression analysis was used to examine relationships among key variables. The results indicate significant improvements in productivity (24.5%), operational efficiency (24.1%), customer satisfaction (24.2%), and return on investment (27.5%). The multiple regression model achieved an R² value of 0.82, while the Random Forest model attained a predictive accuracy of 91%. The study contributes by proposing an AI–business strategy integration framework that extends the Resource-Based View and Strategic Alignment perspectives. Practical implications include supporting managerial decision-making in AI investment allocation, enhancing data analytics capabilities, and developing predictive customer relationship management (CRM) systems. The novelty of this research lies in combining quantitative analysis, data simulation, and predictive modeling to support sustainable digital transformation.
Perbandingan Logistic Regression dan Random Forest untuk Prediksi Respon Pelanggan Asuransi Harliana Harliana; Tito Prabowo; Ady Alzhava Nuary
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 2 (2026): Mei 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i2.214

Abstract

Vehicle insurance companies increasingly rely on data-driven marketing strategies to identify prospective customers who are likely to respond positively to insurance offers. However, customer response prediction is challenging due to class imbalance, where non-responsive customers substantially outnumber responsive ones. This study aims to compare the performance of Logistic Regression and Random Forest models in predicting customer responses to vehicle insurance products using the Synthetic Minority Oversampling Technique (SMOTE). The analysis was conducted using the Vehicle Insurance dataset obtained from Kaggle. Experimental results indicate that Random Forest achieved the best overall performance, with an accuracy of 0.80, a positive-class F1-score of 0.59, and a ROC–AUC score of 0.88. In contrast, Logistic Regression produced a higher positive-class recall of 0.98 but a lower precision of 0.35, indicating a greater tendency to generate false-positive predictions. Feature importance analysis revealed that Previously_Insured, Vehicle_Damage, and Age were the most influential factors affecting customer responses. These findings suggest that the combination of Random Forest and SMOTE provides an effective approach for handling imbalanced data and improving customer response prediction in vehicle insurance marketing campaigns.
Analisis Bibliometrik dan Spasial Border Fiscal Cost Premium Menggunakan VOSviewer Sunaryono Sunaryono; Maria Christiana Iman Kalis; M Irfani Hendri
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 2 (2026): Mei 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i2.219

Abstract

Border regions often face higher public service delivery costs due to geographic isolation, limited accessibility, and inadequate infrastructure. Although research on fiscal equalization, regional inequality, and border studies has expanded significantly, existing studies remain fragmented and lack a comprehensive conceptual framework for understanding fiscal compensation needs in border areas. This study aims to analyze the development of the literature and propose the concept of Border Fiscal Cost Premium through a Systematic Literature Review (SLR) and bibliometric analysis using VOSviewer. The dataset consists of 82 peer-reviewed articles indexed in Scopus from 2001 to 2025. The results identify four major research clusters: Regional Economy and Border Relations, Infrastructure Investment and Economic Development, Fiscal Equalization and Public Finance, and Border Governance and Spatial Inequality. Temporal analysis reveals growing attention to themes such as accessibility, public services, demography, and territorial disadvantage. The literature synthesis indicates that additional fiscal costs in border regions are driven by geographic isolation, limited connectivity, institutional fragmentation, constrained fiscal capacity, and interregional coordination challenges. The proposed Border Fiscal Cost Premium serves as a conceptual framework integrating fiscal, spatial, and governance dimensions to support more adaptive, equitable, and place-based fiscal transfer policies for border regions.

Page 1 of 2 | Total Record : 13

 1   2 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol 24 No 2 (2026): Mei 2026 Vol 24 No 1 (2026): Januari 2026 Vol 23 No 3 (2025): September 2025 Vol 23 No 2 (2025): Mei 2025 Vol 23 No 1 (2025): Januari 2025 Vol 22 No 3 (2024): September 2024 Vol 22 No 2 (2024): Mei 2024 Vol 22 No 1 (2024): Januari 2024 Vol 21 No 3 (2023): September 2023 Vol 21 No 2 (2023): Mei 2023 Vol 21 No 1 (2023): Januari 2023 Vol 20 No 2 (2022): Mei 2022 Vol 20 No 1 (2022): Januari 2022 Vol 19 No 3 (2021): September 2021 Vol 19 No 2 (2021): Mei 2021 Vol 18 No 3 (2020): September 2020 Vol 18 No 2 (2020): Mei 2020 Vol 17 No 3 (2019): September 2019 Vol 17 No 2 (2019): Mei 2019 Vol 17 No 1 (2019): Januari 2019 Vol 16 No 3 (2018): September 2018 More Issue