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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 260 Documents
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Sistem Pendukung Keputusan Pemilihan Karyawan Terbaik Menggunakan Metode SAW Emiliana; Minarwati
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 3 (2026): September 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i3.233

Abstract

Manual employee performance assessment is often subjective, potentially leading to dissatisfaction and errors in decision-making. This study aims to develop a decision support system (DSS) model for selecting the best employee at PT Karya Mandiri Sejahtera by applying the Simple Additive Weighting (SAW) method. The SAW method involves a structured calculation process—specifically, the normalization of the decision matrix based on benefit and cost attributes, followed by a ranking process using a weighted linear combination. The study evaluates five employee alternatives based on four criteria: discipline, work quality, responsibility, and the number of late arrivals. Test results demonstrate that the SAW method consistently produces alternative rankings that are mathematically sound. The alternative with the highest preference value (V) is designated as the best employee. This research confirms that implementing the SAW algorithm can enhance the objectivity and efficiency of the decision-making process within the organization.
Deteksi Ujaran Kebencian Bahasa Indonesia Menggunakan Fine-TuningIndoBERT Berbasis Transformer Minarwati; Meiskey Rambu Dini Naomi
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 3 (2026): September 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i3.234

Abstract

Hate speech on Indonesian social media continues to increase, creating a need for automated systems capable of accurately classifying textual content. This study proposes hate speech detection using the pre-trained IndoBERT transformer model through fine-tuning. Unlike previous studies that combine transformers with additional architectures such as CNN or BiLSTM, this study directly evaluates IndoBERT without additional deep learning layers. The dataset consists of 13,169 Indonesian tweets annotated into Hate Speech (HS) and Non-Hate Speech (Non-HS) categories. The dataset was divided into 80% training, 10% validation, and 10% testing data. The model was trained for three epochs using the AdamW optimizer with a learning rate of 2e-5 and a batch size of 8. Experimental results show that IndoBERT achieved 90.43% accuracy, 90.11% precision, 90.36% recall, and a 90.23% F1-score. These results demonstrate that direct fine-tuning of IndoBERT can achieve strong classification performance with a simpler architecture, supporting its potential for automated digital content moderation.
Analisis Sentimen Ulasan Produk Pada Marketplace Shopee Menggunakan Metode Naive Bayes Abelta Cesharilo; Untung Subagyo
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 3 (2026): September 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i3.235

Abstract

Shopee is a widely used marketplace for online transactions, where product reviews allow users to share their opinions on purchased items. The large volume of reviews makes manual analysis difficult, while previous studies often overlook the normalization of informal language. This study analyzes sentiment in Shopee product reviews using Multinomial Naïve Bayes (MNB) with TF-IDF weighting and custom text normalization, comparatively evaluated against Support Vector Machine (SVM) and validated using 5-fold cross-validation. The dataset consists of 500 balanced reviews (250 positive and 250 negative) obtained from Kaggle. Preprocessing includes case folding, cleansing, normalization, tokenization, stopword removal, and stemming. Hold-out evaluation (80:20) shows that SVM achieves 96.00% accuracy, 92.59% precision, 100.00% recall, and a 96.15% F1-score, while MNB achieves 91.00% accuracy, 84.75% precision, 100.00% recall, and a 91.74% F1-score. Five-fold cross-validation yields mean accuracies of 90.80% (±3.25%) for MNB and 89.60% (±2.50%) for SVM. These results indicate that TF-IDF with custom text normalization provides stable classification performance for e-commerce reviews.
Analisis Implementasi Strategi Bisnis Digital melalui Price Discount, Shopee Ads, dan Search Optimization terhadap Kinerja Penjualan Toko BRANI di Platform Shopee: Studi Kasus Implementasi pada UMKM Badrul Abdullah; Purnomo sidiq
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 3 (2026): September 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i3.236

Abstract

The growth of e-commerce platforms has encouraged Micro, Small, and Medium Enterprises (MSMEs) to adopt various digital business strategies to improve competitiveness and sales performance. This study aims to analyze the implementation of Price Discount, Shopee Ads, and Search Optimization strategies on the sales performance of Toko BRANI on the Shopee platform. A descriptive quantitative approach with a case study method was employed. Data were obtained from Shopee Seller Centre for the May–June 2026 period and analyzed based on the number of visitors, product clicks, orders, sales, and product search performance. The results show that Price Discount generated sales of IDR 258,000 from 52 orders, 70 products sold, and 48 customers. Shopee Ads increased product visibility, generating 29,801 ad impressions, 501 clicks, and 89 products sold through advertising campaigns. Meanwhile, Search Optimization generated 1,521 product visitors, 1,282 search clicks, and 377 buyers. Overall, the three strategies improved product visibility, customer engagement, and sales performance. These findings provide empirical insights into the use of marketplace features as data-driven digital business strategies for MSMEs.
Explainable Machine Learning untuk Analisis Kualitas Soal Pilihan Ganda Berdasarkan Indikator Psikometrik dan Efektivitas Pengecoh Hidayattullah, Abdul Madjid Hasanuddin, Murniati, Frankgling Nusa; Abdul Madjid Hasanuddin; Murniati; Frankgling Nusa
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 3 (2026): September 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i3.240

Abstract

Multiple-choice item quality analysis is essential for educational assessment quality assurance, yet conventional psychometric analysis relies on examinee response data that are often unavailable during item development. This study develops an explainable machine learning framework based on simulated responses to analyze multiple-choice item quality using 10,962 CommonsenseQA items. The dataset serves as a computational testbed due to its consistent five-option format, available answer keys, large scale, and plausible distractors, rather than as a substitute for validated educational item banks. Responses from 200 virtual respondents per item were probabilistically simulated based on answer-key weighting and surface-level distractor plausibility. Four psychometric indicators—difficulty index, discrimination index, distractor effectiveness, and distractor entropy—combined with five surface linguistic features were used to train XGBoost to classify item quality as poor, moderate, or good. Five-fold cross-validation yielded a weighted F1-score of 0.9987 ± 0.0011 and accuracy of 0.9987 ± 0.0011. Feature importance and SHAP identified distractor entropy and discrimination index as dominant predictors. The near-perfect performance is not interpreted as external predictive validity because the target and primary predictors derive from the same simulation mechanism. This framework provides a transparent, auditable proof-of-concept for preliminary item-bank evaluation before empirical testing.
Pemodelan Peramalan Jumlah Pernikahan Menggunakan ARIMA Berbasis Data Historis pada KUA Galur Eko Riswanto; Hasan Ibrahim
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 3 (2026): September 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i3.245

Abstract

The Galur Office of Religious Affairs (KUA) routinely records marriage data but has not optimally utilized it for operational planning. The absence of predictive analysis complicates the allocation of marriage registrars and administrative resources, particularly during fluctuations in marriage applications. This study aims to develop a forecasting model for monthly marriages using the Autoregressive Integrated Moving Average (ARIMA) method to support data-driven decision-making. Historical data covering 120 months (January 2016–December 2025) were divided into 80% training and 20% testing data. The Augmented Dickey-Fuller (ADF) test indicated stationarity at level; therefore, first-order differencing was not required but was retained as a candidate specification. Based on residual diagnostics and the lowest AIC/BIC values, ARIMA(0,1,1) was selected as the best model. Testing produced a Mean Absolute Error (MAE) of 6.84 and a Root Mean Squared Error (RMSE) of 8.37. The final model forecasts a constant 13 marriages per month throughout 2026. Although its point-forecast accuracy did not consistently outperform a simple baseline, ARIMA provides a statistical inference framework that can support proactive staffing and administrative resource planning at KUA Galur.
Evaluasi Keamanan Informasi Menggunakan COBIT 2019 APO13 dalam Mendukung Kualitas Layanan TI di Poltekkes Kemenkes Bengkulu Zulmi Gurhono; Alva Hendi Muhammad; Asro Nasiri
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 3 (2026): September 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i3.247

Abstract

The quality of information technology services in higher education institutions depends on well-designed information security governance. Poltekkes Kemenkes Bengkulu manages academic and health-related data exposed to cybersecurity threats but has not implemented a standardized IT governance framework to determine information security priorities. This study aims to design an IT governance system and prioritize governance and management objectives using the COBIT 2019 Governance System Design Workflow, with APO13–Managed Security as the primary focus. Ten Design Factors were analyzed based on institutional conditions through interviews, observation, and document review. The results identified 17 priority objectives among the 40 COBIT 2019 governance and management objectives, led by BAI10–Managed Configuration (score 100), APO13–Managed Security (90), and APO12–Managed Risk (85). Capability level 4 was targeted for the nine highest-priority objectives, including APO13, driven by logical attack risks (DF3), a High threat landscape (DF5), and High compliance requirements (DF6). Operational recommendations include strengthening information security policies (ISMS), security incident and risk management, and access controls. This study is limited to priority determination and target capability levels; therefore, future research should assess actual capability levels and conduct gap analysis.
Optimasi Rekomendasi Destinasi Wisata Menggunakan Metode CBF Dan GIS Dengan Pendekatan Iterative Ahmad Rifa'i; Hadi Zakaria
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 3 (2026): September 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i3.249

Abstract

The Jakarta Provincial Tourism and Creative Economy Office plays a key role in managing and developing tourist destinations, including providing information through Tourist Information Centers (TICs). However, the existing manual recommendation process does not adequately accommodate tourist preferences, while visitor feedback is not centrally documented. This study aims to develop a web-based tourist destination recommendation system that provides preference-based recommendations, geographic information, and feedback features to support destination evaluation. Content-Based Filtering (CBF) is applied by matching user preferences with destination characteristics using semantic embeddings and cosine similarity. A Geographic Information System (GIS) provides location and route information, while the system is developed using an Iterative Development approach. Recommendation performance was evaluated using Precision and Recall across five query scenarios at K=1, K=3, K=5, and K=10. The results show an average precision of 1.00 at K=1, K=3, and K=5, and 0.86 at K=10. Average recall increased from 0.09 at K=1 to 0.69 at K=10. User evaluation involving 20 respondents achieved an average score of 83.4%. The system assists tourists in finding destinations based on their preferences while providing location, route, and feedback information.
Evaluasi Information Gain pada Random Forest, Decision Tree, dan KNN Klasifikasi Tanaman Dina Andayati; Suraya; Muhammad Sholeh; Suparyanto
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 3 (2026): September 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i3.262

Abstract

Selecting suitable crop types based on soil and environmental conditions is essential for improving agricultural productivity. Machine learning enables crop classification using soil and climate characteristics, but using all features may increase model complexity without necessarily improving performance. This study analyzes the effect of Information Gain-based feature selection on Random Forest, Decision Tree, and K-Nearest Neighbors (KNN) for crop classification. The farming.csv dataset contains 2,200 samples, seven input features—nitrogen (N), phosphorus (P), potassium (K), temperature, humidity, pH, and rainfall—and 22 crop classes. The experiment employed an 80:20 train-test split with random_state 42. Feature selection using mutual_info_classif with a threshold >1.0 reduced the features to six: humidity, K, rainfall, P, temperature, and N. Model performance was evaluated using accuracy, precision, recall, and F1-score. Using all features, Random Forest, Decision Tree, and KNN achieved accuracies of 99.32%, 98.64%, and 97.05%, respectively. After feature selection, Random Forest and Decision Tree achieved 99.09%, while KNN remained at 97.05%. These results indicate that Information Gain can reduce the feature set by one feature while maintaining nearly unchanged classification performance.
Kajian Literatur Sistematis terhadap Penerapan Framework COBIT 5 Tata Kelola Teknologi Informasi Ida Kumala Sari; Cahaya Muzaddidah; Suhirman; Joko Sutopo
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 3 (2026): September 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i3.263

Abstract

Information technology (IT) has become a strategic element across various sectors, supporting operational activities, service effectiveness, and organizational goals. However, poorly managed IT utilization can create various risks. Therefore, IT governance is essential to ensure that IT provides measurable value to organizations. COBIT 5 is a widely used framework for evaluating and improving IT governance comprehensively. This study employs a Systematic Literature Review (SLR) method by analyzing eight scientific publications on COBIT 5 implementation published between 2020 and 2025. The analysis involved identification, selection, quality assessment, and synthesis of relevant studies. The results show that COBIT 5 implementation across various organizations still faces challenges, particularly in business process integration, clarity of roles and responsibilities, and maturity level measurement. Many organizations remain at maturity levels 1–3, indicating that IT processes are implemented but are not yet fully documented or optimally managed. This study provides an overview of IT governance practices across various sectors, identifies trends in the application of COBIT 5 domains, and presents recommendations for improving IT governance implementation in the future.

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