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Jurnal Informatika dan Teknologi Komputer ( J-ICOM)
Published by Universitas Samudra
ISSN : 27752089     EISSN : 27747115     DOI : https://doi.org/10.33059/j-icom.v2i1.3417
Core Subject :
Jurnal J-ICOM (Jurnal Informatika dan Teknologi Komputer) dimaksudkan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian Rekayasa Sistem, Teknik Informatika/Teknologi Informasi, Manajemen Informatika dan Sistem Informasi. Sebagai bagian dari semangat menyebarluaskan ilmu pengetahuan hasil dari penelitian dan pemikiran untuk pengabdian pada Masyarakat luas dan sebagai sumber referensi akademisi di bidang Teknologi dan Informasi. Jurnal J-ICOM (Jurnal Informatika dan Teknologi Komputer) menerima artikel ilmiah dengan lingkup penelitian pada: Rekayasa Sistem Sistem Pakar Sistem Penunjang Keputusan Data Mining Kecerdasan Buatan Jaringan Komputer Teknik Komputer Pengolahan Citra Algoritma Genetik Perancangan Sistem Informasi Business Intelligence and Knowledge Management Database System Big Data Enterprise Computing Cloud Computing Technology Management Topik kajian lainnya yang relevan Dengan artikel yang memiliki sitasi primer dan tidak pernah dipublikasikan secara online atau versi cetak sebelumnya.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 133 Documents
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Identifikasi Pola Fraud pada Ekosistem Pembayaran Digital menggunakan Metode Isolation Forest Akbar, M. Willi; Kusuma Ningrum, Septiani; Afrina, Mira; Ibrahim, Ali
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 7 No 01 (2026): Jurnal Informatika dan Teknologi Komputer ( J-ICOM)
Publisher : E-Jurnal Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/j-icom.v7i01.13622

Abstract

Transformasi menuju ekonomi digital di Indonesia dihadapkan pada tantangan krusial berupa meningkatnya serangan fraud yang semakin canggih. Penelitian ini mengajukan sebuah pendekatan unsupervised learning untuk mengenali pola serangan fraud generasi baru sebagai dasar penguatan kapasitas supervisi institusional. Penelitian ini berfokus pada identifikasi anomali tanpa bergantung pada label historis yang ada dengan memanfaatkan algoritma ensemble Isolation Forest. Model berhasil memetakan karakteristik transaksi yang mencurigakan berkat penerapan rekayasa fitur yang mendalam, yang mencakup analisis perilaku, korelasi alamat, dan ekstraksi sinyal dari IP. Hasil evaluasi menunjukkan bahwa pendekatan unsupervised ini mampu mengidentifikasi 18% dari total kasus fraud yang telah dilabelkan, membuktikan relevansinya dalam menangkap sinyal serangan yang sesungguhnya. Lebih penting lagi, analisis kualitatif terhadap anomali yang ditemukan berhasil mengkarakterisasi sebuah Pola Serangan Senyap, yaitu kombinasi multi-faktor risiko yang berpotensi terlewatkan oleh sistem deteksi konvensional. Temuan ini menyajikan sebuah wawasan baru bagi institusi regulator untuk beralih dari supervisi reaktif ke penemuan ancaman proaktif, yang pada akhirnya mendukung terciptanya ekosistem keuangan digital yang aman dan berkelanjutan sejalan dengan Tujuan Pembangunan Berkelanjutan (SDGs).
Analisis Kepuasan Masyarakat Pada Layanan SIM Online Menggunakan Metode SERVQUAL di SATLANTAS POLRES Garut Nurul Kiftiyah
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 7 No 01 (2026): Jurnal Informatika dan Teknologi Komputer ( J-ICOM)
Publisher : E-Jurnal Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/j-icom.v7i01.13439

Abstract

Public service is a key responsibility of the government in meeting citizens’ needs. In the digital era, service quality is assessed not only by speed and accuracy but also by public satisfaction. One such innovation is the Online Driving License (SIM Online) service, introduced by the Indonesian National Police through Satlantas. While designed to simplify license issuance and renewal, its effectiveness must be evaluated.This internship research analyzes the service quality of SIM Online at Satlantas Polres Garut using the SERVQUAL method, covering Tangibles, Reliability, Responsiveness, Assurance, and Empathy. Data were collected from 100 respondents through questionnaires, supported by observation and short interviews.The results show an average expectation score of 4.62 and a perception score of 4.18, with a gap of -0.44, indicating the service has not fully met expectations. The largest gap lies in Responsiveness (-0.55), while the smallest is in Assurance (-0.35).In conclusion, SIM Online has improved service accessibility, but several aspects need enhancement, particularly responsiveness. These findings provide input for Satlantas Polres Garut to improve digital-based public services.
Visualisasi Open Governance Data untuk Analisis Keterkaitan Human Capital Index dan e-Government Development Index Cut Alna Fadhilla
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 7 No 01 (2026): Jurnal Informatika dan Teknologi Komputer ( J-ICOM)
Publisher : E-Jurnal Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/j-icom.v7i01.14806

Abstract

Open Governance Data is an approach that supports the open, integrated, and transparent management of data to generate valuable information for data-driven decision-making. The utilization of open data has become increasingly important in supporting digital government transformation and achieving sustainable development goals. This study aims to analyze the relationship between the Human Capital Index (HCI) and the E-Government Development Index (EGDI) using an Open Governance Data approach to support sustainable development policymaking. The research data were obtained from international open data sources, including the United Nations E-Government Survey 2020, 2022, and 2024, as well as Human Capital Index data from the World Bank. The study employed a quantitative approach consisting of data collection, data integration, data preprocessing, descriptive analysis, comparative analysis, and data visualization. The results indicate that countries with higher Human Capital Index scores tend to achieve better E-Government Development Index performance. These findings suggest that human capital quality plays a significant role in supporting the success of digital government transformation. Furthermore, the implementation of Open Governance Data enables the integration of multiple open data sources to produce comprehensive information for policy planning, monitoring, and evaluation. This study proposes an Open Governance Data-based analytical model that can support more effective, transparent, and evidence-based sustainable development decision-making
Imbalance-Aware XGBoost-SHAP untuk Klasifikasi Multikelas Intrusi Jaringan Berbasis Web Rifqi M Rifqi; Hana; Candhy
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 7 No 01 (2026): Jurnal Informatika dan Teknologi Komputer ( J-ICOM)
Publisher : E-Jurnal Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/j-icom.v7i01.15188

Abstract

Network intrusion detection requires models that are accurate for imbalanced traffic and interpretable for administrative use. This study develops a web-based explainable intrusion detection prototype using imbalance-aware multiclass XGBoost and SHAP. The CICIDS2017 flow data were processed into 2,231,759 records with 77 numerical features and seven labels: BENIGN, Bot, Brute Force, DDoS, DoS, PortScan, and Web Attack. Data were split using an 80:20 stratified scheme, while balanced sample weights were applied only to the training set. The trained XGBoost model was evaluated using accuracy, macro precision, macro recall, macro F1-score, Matthews correlation coefficient, and confusion matrix. Model interpretation was presented through global SHAP, class-specific SHAP, local SHAP, and false-positive/false-negative analysis on a Flask dashboard. The model achieved 99.91% accuracy, 92.83% macro precision, 98.97% macro recall, 95.30% macro F1-score, and 0.9967 MCC. Functional local prediction testing on seven scenarios produced four correct classifications and three false negatives due to the prototype mode using 12 top SHAP features while imputing the remaining features with training medians. The results show that the proposed prototype can support explainable network intrusion monitoring while still requiring full-feature batch prediction for operational evaluation. Keywords: intrusion detection, XGBoost, SHAP, class imbalance, web dashboard
Analisis Kualitas Pelayanan Pembuatan KK Melalui Aplikasi SIAK Pada Disdukcapil Kabupaten Sumba Barat Ardiyanto Dapadeda
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 7 No 01 (2026): Jurnal Informatika dan Teknologi Komputer ( J-ICOM)
Publisher : E-Jurnal Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/j-icom.v7i01.13420

Abstract

This study aims to analyze the quality of family card issuance services through the Population Administration Information System (SIAK) application at the Population and Civil Registration Office (Disdukcapil) of West Sumba Regency. The background of this study is based on the importance of effective and efficient public services, especially in the management of population documents, which are a basic need of the community. The research method used was quantitative. Data collection techniques were carried out through the distribution of questionnaires. Data analysis was performed using the Service Quality (Servqual) service quality indicator with five dimensions, namely tangible (physical evidence), reliability, responsiveness, assurance, and empathy. The results of the study show that the quality of service is very satisfactory. Overall, the quality of service for family card (KK) issuance on the SIAK Application of the Population and Civil Registration Office of West Sumba Regency is considered satisfactory based on the dimensions of Responsiveness (GAP 0.23) and Empathy (GAP 0.22) as the dimensions with the largest positive gaps, while the dimensions of Reliability (GAP 0.10) and Tangible (GAP 0.11) showed the lowest positive GAP values.
Analisis Efektivitas Sistem Arsip Digital Hotel Berbasis Web Di Sekuto Inn Guesthouse Yusuf Faishal; Rudi Heryanto
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 7 No 01 (2026): Jurnal Informatika dan Teknologi Komputer ( J-ICOM)
Publisher : E-Jurnal Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/j-icom.v7i01.14178

Abstract

Penelitian ini bertujuan menganalisis efektivitas Sistem Informasi Arsip Digital Dokumen Operasional Hotel berbasis web yang dikembangkan menggunakan Visual Studio Code dan basis data Firebase di Sekuto Inn Guest House Tegal. Penelitian menggunakan pendekatan kuantitatif dengan metode deskriptif evaluatif. Data diperoleh melalui observasi, kuesioner skala Likert, dan dokumentasi dengan teknik total sampling terhadap pengguna sistem. Variabel penelitian meliputi kualitas sistem, kualitas informasi, kualitas layanan, dan kepuasan pengguna. Analisis data dilakukan menggunakan regresi linier berganda setelah melalui uji validitas, reliabilitas, dan asumsi klasik. Hasil penelitian menunjukkan bahwa ketiga variabel independen secara simultan berpengaruh kuat terhadap kepuasan pengguna dengan koefisien determinasi sebesar 72,4%. Secara parsial, kualitas layanan berpengaruh positif dan signifikan serta menjadi faktor dominan, sedangkan kualitas sistem dan kualitas informasi tidak berpengaruh signifikan. Temuan ini menegaskan bahwa efektivitas sistem informasi arsip digital lebih dipengaruhi oleh kualitas layanan dalam mendukung kepuasan pengguna.
Penerapan Teknologi Digital di Bank Sampah nia_78 Sumarni; Y.I Supriyanti; Fanro Fanro; P.A Moento; Thoding Bermani; D.I.P. Indarti
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 7 No 2 (2026): Jurnal Informatika dan Teknologi Komputer ( J-ICOM)
Publisher : E-Jurnal Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/j-icom.v7i2.14224

Abstract

Digital transformation has created new opportunities to improve community-based waste management through the use of information technology. In Indonesia, waste banks have begun adopting website-based systems to support administrative recording, information sharing, and digital management of waste transactions. However, the success of such technological innovations largely depends on how community members perceive and accept the technology. This study aims to analyze the factors influencing the acceptance of digital technology in community-based waste bank management using the Technology Acceptance Model (TAM). A quantitative approach was employed using a survey of 90 members of the Arga Tirta Waste Bank in North Bengkulu. It analyzed using descriptive statistics and multiple linear regression. The results show that attitude toward use (β = 0.355) and perceived usefulness (β = 0.250) have positive and significant effects on behavioral intention to use. In contrast, perceived ease of use (β = 0.0840) does not significantly influence users’ intention to adopt the technology. The model explains only 12.2% of the variance in behavioral intention, indicating limited explanatory power. These findings suggest that technology adoption in rural community contexts is not solely determined by technical aspects such as system usability. Social perceptions, collective attitudes, and institutional support may play more substantial roles in shaping users’ willingness to adopt digital systems. This study contributes to the literature by demonstrating the limited explanatory capacity of TAM in community-based environmental management and highlighting the importance of social and contextual factors in shaping digital technology adoption in rural waste management initiatives.
Pengembangan Sistem Visualisasi 3D Berbasis Web Menggunakan Data LiDAR dari iPhone 17 Pro Yustina Cheline J Owa; Septa Erik Prabawa; Ketut Tomy Suhari
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 7 No 01 (2026): Jurnal Informatika dan Teknologi Komputer ( J-ICOM)
Publisher : E-Jurnal Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/j-icom.v7i01.14237

Abstract

This study develops a web-based 3D visualization system for a public wall mural captured using the LiDAR sensor of the iPhone 17 Pro. The workflow includes data acquisition, point cloud and mesh generation, conversion to glTF, packaging into 3D Tiles, semantic metadata integration, and interactive web visualization. The dataset contains 6,079,580 XYZRGB points with a local spatial extent of approximately 40.24 m × 5.93 m × 35.61 m. The reconstructed glTF model consists of 801,949 vertices and about 1,372,979 triangles, while the final 3D Tiles package uses implicit octree tiling with 89 GLB tiles across five levels. Results show that the conversion pipeline significantly improves web readiness, reducing data size by about 93.33% from raw XYZRGB to the final 3D Tiles package. External JSON-based metadata also supports dataset description and semantic grouping within the viewer. The novelty of this study lies in the end-to-end integration of low-cost mobile LiDAR acquisition, web-oriented geometric optimization, implicit tiling, and semantic 3D publication for documenting public art. These findings demonstrate that smartphone-based LiDAR can provide a practical and low-cost solution for 3D digitization and web publication of mural objects.
Deteksi Pencurian Listrik pada Data Smart Meter Menggunakan Pembelajaran Mesin Rafli Alfian; Edmund Ucok Armin
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 7 No 2 (2026): Jurnal Informatika dan Teknologi Komputer ( J-ICOM)
Publisher : E-Jurnal Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/j-icom.v7i2.14807

Abstract

Electricity theft is a primary source of non-technical losses (NTL) in power distribution systems, causing substantial economic damage to utilities. This study develops a machine learning approach to detect electricity theft using real smart meter data from the State Grid Corporation of China (SGCC), comprising 42,372 consumers over 1,034 days with an 8.53% theft rate. Nineteen statistical features were engineered from the daily consumption profiles after handling 25.64% missing values through linear interpolation and outliers through the three-sigma rule. Four classifiers, namely Logistic Regression, Decision Tree, Random Forest, and histogram-based Gradient Boosting, were trained with the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance and evaluated on a stratified hold-out set. The Gradient Boosting model achieved the best performance with an ROC-AUC of 0.750, an F1-score of 0.323, and a recall of 0.416, outperforming the other classifiers, while cross-validation confirmed the stability of the results. Feature importance analysis revealed that the missing-data ratio, the low-consumption ratio, and the longest zero-consumption run are the most discriminative indicators of theft. The results show that statistical feature engineering combined with ensemble learning provides an effective and computationally efficient method for electricity theft detection in smart grids.
Klasifikasi Kepuasan Pelanggan pada Layanan Servis Otomotif Menggunakan Naive Bayes: Studi Kasus di PT AUTO2000 Sukun Malang Vanesa Putri Maya Hasyim; Wahyu Teja Kusuma; Mochammad Anshori
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 7 No 2 (2026): Jurnal Informatika dan Teknologi Komputer ( J-ICOM)
Publisher : E-Jurnal Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/j-icom.v7i2.14964

Abstract

Customer satisfaction serves as a crucial indicator for assessing the quality of after sales services. PT AUTO2000 Sukun routinely gathers customer feedback through satisfaction surveys. However, the manual evaluation process is both time consuming and susceptible to subjective judgment. This study seeks to classify customer satisfaction based on customer comments using the Multinomial Naive Bayes algorithm. The dataset comprises 108 customer reviews accompanied by manually assigned positive and negative sentiment labels. The research procedure consisted of text preprocessing, including case folding, tokenization, stopword removal, and stemming, followed by feature extraction using Term Frequency Inverse Document Frequency (TF IDF). The dataset was then divided into 70% training data and 30% testing data. Synthetic Minority Over sampling Technique (SMOTE) was applied exclusively to the training data to alleviate class imbalance. Model performance was assessed using accuracy, precision, recall, F1 score, and a confusion matrix. The findings reveal that the Multinomial Naive Bayes algorithm attained an accuracy of 76 percent. For the negative class, the model achieved a precision of 79%, a recall of 86%, and an F1 score of 83%. For the positive class, it obtained a precision of 67%, a recall of 55%, and an F1 score of 60%. These findings suggest that the Multinomial Naive Bayes algorithm exhibits satisfactory capability in classifying customer satisfaction. Nevertheless, the model still encounters challenges in accurately recognizing all instances belonging to the positive class.

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