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Progresif: Jurnal Ilmiah Komputer
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Banjarbaru
ISSN : 02163284     EISSN : 26850877     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
Progresif: Jurnal Ilmiah Komputer adalah Jurnal Ilmiah bidang Komputer yang diterbitkan secara periodik dua nomor dalam satu tahun, yaitu pada bulan Februari dan Agustus. Redaksi Progresif: Jurnal Ilmiah Komputer menerima Artikel hasil penelitian atau atau artikel konseptual bidang Komputer.
Arjuna Subject : -
Articles 508 Documents
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Analisis Kesuksesan Video Game Simulasi Depresi dan Kekeluargaan Menggunakan Model D&M ISS Cen, Li; Forrensa, Febrianty; Tan, Tony
Progresif: Jurnal Ilmiah Komputer Vol 22, No 1 (2026): Januari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i1.3442

Abstract

Depression is a critical mental health problem that is often difficult to understand empathetically, creating a need for interactive media to communicate psychological experiences. This study analyzes the success of a simulation video game addressing depression and family themes using the DeLone and McLean Information System Success Model. A quantitative experimental method involved 124 participants aged 18–21 who played Ruang Hampa and completed questionnaires based on their gameplay experience. The variables examined included System Quality, Information Quality, Service Quality, Intention to Use, User Satisfaction, and Net Benefits, analyzed using multiple linear regression. The study reveals that Information Quality and Service Quality significantly influence Intention to Use, which subsequently increases Net Benefits. In contrast, System Quality and User Satisfaction do not have a significant direct effect. These findings indicate that narrative and informational elements exert a greater impact than technical system aspects in achieving the success of psychologically themed educational games.Keywords: Video Game; Depression Simulation; Family; DeLone & McLean Model; Mental Health AbstrakDepresi merupakan isu kesehatan mental yang serius dan masih sulit dipahami secara empatik oleh masyarakat, sehingga diperlukan media interaktif untuk menyampaikan pengalaman psikologis. Penelitian ini bertujuan untuk menganalisis kesuksesan video game simulasi bertema depresi dan kekeluargaan menggunakan model DeLone & McLean. Metode eksperimen kuantitatif melibatkan 124 responden berusia 18–21 tahun yang memainkan video game Ruang Hampa dan mengisi kuesioner sesuai pengalaman bermain. Variabel yang dianalisis mencakup Kualitas Sistem, Kualitas Informasi, Kualitas Layanan, Niat Penggunaan, Kepuasan Pengguna, dan Manfaat Bersih, dengan analisis regresi linier berganda. Penelitian ini menemukan bahwa Kualitas Informasi dan Kualitas Layanan berpengaruh positif signifikan terhadap Niat Penggunaan yang kemudian meningkatkan Manfaat Bersih. Sebaliknya, Kualitas Sistem tidak menunjukkan pengaruh signifikan, dan Kepuasan Pengguna tidak berpengaruh langsung terhadap Manfaat Bersih. Simpulan penelitian ini menunjukkan bahwa aspek naratif dan informasional lebih menentukan keberhasilan game edukatif bertema psikologis dibandingkan aspek teknis sistem.Kata kunci: Video Game; Simulasi Depresi; Kekeluargaan; Model DeLone & McLean; Kesehatan Mental
Prediksi Kelayakan Kredit Nasabah Dengan Penerapan Cost-Sensitive Random Forest Lucretia, Jolyn; Hermanto, Dedy
Progresif: Jurnal Ilmiah Komputer Vol 22, No 1 (2026): Januari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i1.3401

Abstract

The high risk of credit default and class imbalance in customer data pose major challenges in developing accurate credit scoring systems. This condition causes predictive models to be biased toward the majority class, thereby reducing the ability to detect high-risk borrowers. This study develops a credit scoring model for imbalanced data using the Synthetic Minority Oversampling Technique (SMOTE) and cost-sensitive Random Forest with hyperparameter optimization via GridSearchCV. The dataset consists of 32,581 customer records. Experimental results show that the best configuration with n_estimators = 200 achieves a cross-validation F1-score of 0.813750. On the test data, the model attains an accuracy of 0.927267, precision of 0.911458, recall of 0.738397, and an F1-score of 0.815851, indicating improved and more balanced detection of high-risk borrowers.Keywords: Random Forest; SMOTE; Cost-sensitive learning; GridSearchCV AbstrakTingginya risiko gagal bayar kredit dan ketidakseimbangan kelas pada data nasabah menjadi tantangan utama dalam pengembangan sistem credit scoring yang akurat. Kondisi ini menyebabkan model prediksi cenderung bias terhadap kelas mayoritas sehingga kemampuan deteksi debitur berisiko menjadi kurang optimal. Penelitian ini mengembangkan model credit scoring pada data tidak seimbang menggunakan Synthetic Minority Oversampling Technique (SMOTE) dan Cost-Sensitive Random Forest dengan optimasi hyperparameter GridSearchCV. Dataset yang digunakan berjumlah 32.581 data nasabah. Hasil pengujian menunjukkan konfigurasi terbaik dengan n_estimators = 200 menghasilkan F1-score validasi silang sebesar 0,813750. Pada data uji, model mencapai akurasi 0,927267, precision 0,911458, recall 0,738397, dan F1-score 0,815851, yang menunjukkan peningkatan kemampuan deteksi debitur berisiko secara lebih seimbang.Kata kunci: Random Forest; SMOTE; Cost-sensitive learning; GridSearchCV.
Klasifikasi Motif Kain Batik Nitik Menggunakan Support Vector Machine dengan Ekstraksi Fitur EfficientNet-B0 Saputra, Dika; Yohannes, Yohannes
Progresif: Jurnal Ilmiah Komputer Vol 22, No 1 (2026): Januari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i1.3507

Abstract

Nitik Batik is an Indonesian cultural heritage with complex geometric dot patterns, yet its digitalization and preservation efforts remain limited. This study aims to develop an automatic classification system for 60 Nitik Batik motifs using a combination of EfficientNet-B0 as a feature extractor and Support Vector Machine (SVM) as a classifier. The Batik Nitik 960 Dataset was expanded from 960 to 1,920 images with rotation augmentation. Experiments were conducted with 10-fold cross-validation and evaluation on separate test data. Results show that the model without augmentation achieved 55.71% accuracy, 90.06% macro precision, 55.71% recall, and 65.29% F1-score. Blur augmentation with 30% probability reduced accuracy to 49.29% although it decreased overfitting by 6.63%. SVM parameters were set to C=0.3 and gamma=0.01 to improve regularization. This study concludes that the combination of EfficientNet-B0 and SVM is effective for multi-class batik classification, but blur augmentation is unsuitable for detail-rich textile data. Future research recommendations include exploring geometric augmentation and more advanced feature extractor architectures.Kata kunci: Nitik Batik; Image classification; EfficientNet-B0; Support Vector Machine; augmentation. AbstrakBatik Nitik merupakan warisan budaya Indonesia dengan motif geometris berbentuk titik yang kompleks, namun upaya digitalisasi dan pelestariannya masih terbatas. Penelitian ini bertujuan mengembangkan sistem klasifikasi otomatis untuk 60 motif Batik Nitik menggunakan kombinasi EfficientNet-B0 sebagai ekstraktor fitur dan Support Vector Machine (SVM) sebagai klasifikator. Dataset Batik Nitik 960 Dataset diperluas dari 960 menjadi 1.920 citra dengan augmentasi rotasi. Eksperimen dilakukan dengan skema 10-fold cross-validation dan evaluasi pada data uji terpisah. Hasil menunjukkan bahwa model tanpa augmentasi mencapai akurasi 55,71%, precision macro 90,06%, recall 55,71%, dan F1-score 65,29%. Augmentasi blur 30% justru menurunkan akurasi menjadi 49,29% meskipun mengurangi overfitting sebesar 6,63%. Parameter SVM diatur C=0,3 dan gamma=0,01 untuk meningkatkan regularisasi. Penelitian ini menyimpulkan bahwa kombinasi EfficientNet-B0 dan SVM efektif untuk klasifikasi batik multikelas, namun augmentasi blur tidak sesuai untuk data tekstur kaya detail. Rekomendasi penelitian selanjutnya adalah eksplorasi augmentasi geometris dan arsitektur feature extractor yang lebih advance.Kata kunci: Batik Nitik; Klasifikasi citra; EfficientNet-B0; Support Vector Machine; Augmentasi.
Implementasi Penetasan Telur Otomatis berbasis IoT di Dapur Kalkun Undaan Tengah Kudus Arif, Diki; Wijayanti, Esti; Maharani, Rizkysari Mei
Progresif: Jurnal Ilmiah Komputer Vol 22, No 1 (2026): Januari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i1.3280

Abstract

The growing demand for turkey meat and eggs is not supported by sufficient chick availability, as most hatching still relies on manual methods with low success rates. This study develops an Internet of Things (IoT)-based turkey egg incubator system to help farmers monitor and control incubation automatically and in real time. The system uses a NodeMCU ESP8266 microcontroller, a DHT11 sensor for temperature and humidity, a relay for the heater, and a servo motor for egg rotation. A web interface enables remote monitoring via the internet. Tests using whitebox, blackbox, and user acceptance methods showed that the system maintains the ideal temperature of 37–38°C, regulates humidity, and performs automatic egg rotation. The system effectively supports incubation management, although improvements such as automatic notifications, backup sensors, and historical data visualization are still recommended for future development.Keywords: turkey egg incubator; Internet of Things (IoT); Monitoring and control; Automated hatching system.AbstrakPermintaan terhadap daging dan telur ayam kalkun terus meningkat, namun ketersediaan anak ayam masih terbatas karena proses penetasan sebagian besar dilakukan secara manual dengan tingkat keberhasilan yang rendah. Penelitian ini bertujuan mengembangkan sistem inkubator telur ayam kalkun berbasis Internet of Things (IoT) untuk membantu peternak memantau dan mengendalikan kondisi inkubasi secara otomatis serta real-time. Sistem dibangun menggunakan mikrokontroler NodeMCU ESP8266 sebagai pengendali utama yang terhubung dengan sensor DHT11 untuk membaca suhu dan kelembaban, relay untuk mengatur pemanas, serta motor servo untuk memutar telur. Antarmuka web disediakan agar pengguna dapat memantau kondisi inkubator dari jarak jauh. Pengujian dilakukan melalui metode whitebox, blackbox, dan User Acceptance Test (UAT), yang menunjukkan bahwa sistem mampu menjaga suhu ideal 37–38°C, mengatur kelembaban sesuai kebutuhan, serta melakukan pemutaran telur secara otomatis selama masa inkubasi. Sistem ini efektif meningkatkan efisiensi penetasan, meski masih perlu pengembangan fitur seperti notifikasi otomatis, cadangan sensor, dan visualisasi data historis.Kata kunci: Inkubator telur kalkun; Internet of things; Monitoring dan kendali; Sistem penetasan otomatis
Implementasi Backpropagation Neural Network pada Sistem Electronic Nose untuk Klasifikasi Aroma Teh Arif, M. Aidil; Hidayat, Muhammad; Ridhani, M. Fadli
Progresif: Jurnal Ilmiah Komputer Vol 22, No 1 (2026): Januari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i1.3482

Abstract

Conventional tea aroma quality assessment is subjective and slow. This study aims to design and implement an Arduino Uno-based automatic Electronic Nose (e-nose) system with a TGS sensor array (880, 826, 822, 825) combined with a Backpropagation Neural Network (BPNN) for tea aroma classification. The method includes signal acquisition, normalization, feature extraction, and sensor correlation analysis to form a chemical fingerprint before modeling. Testing with a confusion matrix on three types of tea (black, green, and jasmine) showed performance with an accuracy of 0.71, precision of 0.71, recall of 0.72, and f-measure of 0.71. The results of this study provide an objective, fast, economical, and non-destructive aroma evaluation method and contribute to the development of smart sensor technology to support the competitiveness of Indonesian tea products. The main novelty of this study is the integration of sensor correlation analysis into the modeling pipeline with an end-to-end classification system that combines sensor correlation analysis to optimize the performance of the BPNN model on the tea aroma dataset.Keywords: Arduino; Tea Aroma; Backpropagation; Electronic Nose; TGS Sensor AbstrakPenilaian mutu aroma teh secara konvensional bersifat subjektif dan lambat. Penelitian ini bertujuan merancang dan mengimplementasikan sistem Electronic Nose (e-nose) otomatis berbasis Arduino Uno dengan array sensor TGS (880, 826, 822, 825) yang dikombinasikan Backpropagation Neural Network (BPNN) untuk klasifikasi aroma teh. Metode mencakup akuisisi sinyal, normalisasi, ekstraksi fitur, dan analisis korelasi sensor untuk membentuk chemical fingerprint sebelum pemodelan. Pengujian dengan confusion matrix pada tiga jenis teh (hitam, hijau, wangi melati) menunjukkan performa dengan akurasi 0,71, presisi 0,71, recall 0,72, dan f-measure 0,71. Hasil penelitian memberikan metode evaluasi aroma yang objektif, cepat, ekonomis, dan non destruktif, serta berkontribusi pada pengembangan teknologi sensor cerdas untuk mendukung daya saing produk teh Indonesia. Kebaruan utama penelitian ini adalah pada integrasi analisis korelasi sensor ke dalam pipeline pemodelan dengan sistem klasifikasi end-to-end yang menggabungkan analisis korelasi sensor untuk mengoptimalkan performa model BPNN pada dataset aroma teh.Kata kunci: Arduino; Aroma Teh; Backpropagation; Electronic Nose; Sensor TGS
Penerapan Explainable AI dan Model Stacking Untuk Mengindentifikasi Faktor Risiko Stunting Balita Majid, Annisa Maulana; Nawangsih, Ismasari
Progresif: Jurnal Ilmiah Komputer Vol 22, No 1 (2026): Januari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i1.3549

Abstract

Stunting remains a nutritional problem in children that is difficult to detect early due to its multifactorial causes and the limitations of conventional analytical approaches. This study aims to develop a stacking-based Machine Learning model for stunting prediction and to implement Explainable Artificial Intelligence (XAI) to interpret the risk factors influencing the prediction outcomes. The methods employed include Decision Tree, Random Forest, and Support Vector Machine (SVM) as base learners, and Logistic Regression as the meta-learner in an ensemble stacking framework, with performance evaluated using accuracy, precision, recall, and F1-score. The results indicate that the Decision Tree algorithm and the Stacking method achieved the best performance with 100% accuracy, while the XAI analysis identified current body weight, birth weight, and age as the primary factors influencing stunting prediction.Keywords: Stunting; Stacking; Machine Learning; Explainable Artificial Intelligence AbstrakStunting masih menjadi permasalahan gizi pada anak yang sulit dideteksi secara dini karena dipengaruhi oleh berbagai faktor dan keterbatasan analisis konvensional. Penelitian ini bertujuan mengembangkan model stacking berbasis Machine Learning untuk prediksi stunting serta menerapkan Explainable Artificial Intelligence (XAI) dalam menginterpretasikan faktor-faktor risiko yang berpengaruh terhadap hasil prediksi. Metode yang digunakan meliputi algoritma Decision Tree, Random Forest, Support Vector Machine (SVM) sebagai base learner dan Logistic Regression sebagai meta learner pada ensemble stacking, dengan evaluasi menggunakan accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa algoritma Decision Tree dan penerapan metode Stacking menghasilkan kinerja terbaik dengan tingkat akurasi 100%, sementara analisis XAI mengidentifikasi bahwa berat badan, BB lahir, dan usia merupakan faktor utama yang memengaruhi prediksi stunting.Kata kunci: Stunting; Stacking; Machine Learning; Explainable Artificial Intelligence
Rancang Bangun Dashboard Monitoring Pengiriman Bermasalah pada E-Commerce marta, amelia vidora revita; riadi, aditya akbar; chamid, ahmad abdul
Progresif: Jurnal Ilmiah Komputer Vol 22, No 1 (2026): Januari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i1.3516

Abstract

The rapid growth of e-commerce has increased the volume of orders that must be monitored by sellers, making the handling of delivery delays a major challenge. This study designs and develops a dashboard to monitor problematic shipments on an e-commerce platform by applying a Service Level Agreement (SLA) approach as the basis for evaluating delivery delays. The research adopts a Research and Development (R&D) method with the Waterfall system development model, which includes the stages of requirement analysis, system design, implementation, and functional testing. The developed dashboard is able to automatically classify shipment statuses based on SLA calculations, display indicators of orders at risk of delay, and present delivery data through graphical visualizations. The testing results indicate that the system successfully presents real-time delivery data, proactively detects potential delays, and supports sellers in making operational decisions. This system provides a more effective and measurable SLA-based approach for monitoring e-commerce deliveries.Keywords: Monitoring dashboard; Problematic delivery; Service Level Agreement; E-commerce; Automatic detection.AbstrakPerkembangan e-commerce telah meningkatkan volume pesanan yang harus dipantau oleh seller, sehingga penanganan keterlambatan pengiriman menjadi tantangan utama. Penelitian ini merancang dan membangun sebuah dashboard untuk memonitor pengiriman bermasalah pada platform e-commerce dengan pendekatan Service Level Agreement (SLA) sebagai dasar penilaian keterlambatan pengiriman. Metode yang digunakan adalah Research and Development (R&D) dengan model pengembangan sistem Waterfall, yang meliputi tahapan analisis kebutuhan, perancangan sistem, implementasi, dan pengujian fungsionalitas. Dashboard yang dikembangkan mampu mengklasifikasikan status pengiriman secara otomatis berdasarkan perhitungan SLA, menampilkan indikator pesanan berisiko terlambat, serta menyajikan grafik visualisasi pengiriman. Hasil pengujian menunjukkan bahwa sistem berhasil menampilkan data pengiriman secara real-time, mendeteksi potensi keterlambatan secara proaktif, dan membantu seller dalam pengambilan keputusan operasional. Sistem ini memberikan pendekatan monitoring pengiriman yang lebih efektif dan terukur berbasis SLA.Kata kunci: Dashboard monitoring; Pengiriman bermasalah; Service Level Agreement; E-commerce; Deteksi otomatis
Digitalisasi Manjemen Perkembangan Ayam di PT Muria Jaya Raya Kabupaten Kudus Rizka, Muhammad Ahdanal; Listyorini, Tri; Supriyati, Endang
Progresif: Jurnal Ilmiah Komputer Vol 22, No 1 (2026): Januari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i1.3455

Abstract

This study examined the development of a broiler chicken management information system within a partnership scheme at PT. Muria Jaya Raya in Kudus Regency. The identified problems included manual and fragmented processes in monitoring chicken conditions, recording feed usage, scheduling vaccinations, and planning harvests. The study aimed to design an integrated information system to manage these processes in a structured manner. The research methodology consisted of requirement analysis, system design, database implementation, and system testing using branch coverage–based white box testing, black box testing, and User Acceptance Test. The testing results indicated that all system functions operated according to the design specifications and user requirements. The system improved data organization, facilitated effective monitoring of chicken growth, and supported data-driven decision making. Based on the testing outcomes, the system was considered suitable for operational use in partnership-based broiler farming management.Keywords: Information system; Broiler chicken; Livestock partnership; Growth monitoring; Farm managementAbstrakPenelitian ini mengkaji pengembangan sistem manajemen perkembangan ayam broiler dalam skema kemitraan di PT. Muria Jaya Raya Kabupaten Kudus. Permasalahan yang dihadapi meliputi pemantauan kondisi ayam, pencatatan jenis pakan, penjadwalan vaksinasi, dan perencanaan panen yang masih dilakukan secara manual dan terpisah. Penelitian ini bertujuan merancang sistem informasi yang mampu mengelola data tersebut secara terintegrasi dan terstruktur. Metode penelitian meliputi analisis kebutuhan, perancangan sistem, implementasi basis data, serta pengujian sistem menggunakan white box berbasis branch coverage, black box testing, dan User Acceptance Test. Hasil pengujian menunjukkan bahwa seluruh fungsi sistem berjalan sesuai dengan rancangan dan kebutuhan pengguna. Sistem dinilai mampu meningkatkan keteraturan pencatatan, mempermudah pemantauan perkembangan ayam, serta mendukung pengambilan keputusan berbasis data. Berdasarkan hasil pengujian tersebut, sistem dinyatakan layak digunakan dalam operasional peternakan ayam broiler berbasis kemitraan.Kata kunci: Sistem informasi; Ayam broiler; Kemitraan peternakan; Pemantauan pertumbuhan; manajemen peternakan
Penerapan Content-Based Filtering dan K-Nearest Neighbor pada Sistem Rekomendasi Wisata Probolinggo Arifin, Syamsul; Indahsari, Rina Dewi
Progresif: Jurnal Ilmiah Komputer Vol 22, No 1 (2026): Januari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i1.3508

Abstract

Probolinggo Regency has a wide range of tourism destinations; however, the limited dissemination of tourism information makes it difficult for tourists to determine suitable travel destinations. This study aims to develop a tourism destination recommendation system to assist users in obtaining recommendations based on their preferences. The system is developed using the Content-Based Filtering (CBF) method with TF-IDF weighting and cosine similarity, as well as the K-Nearest Neighbor (KNN) algorithm based on cosine distance. The system process includes tourism data collection, text feature extraction and weighting, similarity calculation, and the presentation of main destination recommendations and similar destinations. System evaluation using precision and recall metrics shows that the Content-Based Filtering (CBF) and K-Nearest Neighbor (KNN) methods achieve precision values of 93.75% and 96.00%, respectively, with both methods obtaining a recall value of 100%, indicating that the system is able to provide relevant tourism destination recommendations.Keywords: Recommendation System; Probolinggo Tourism; Content-Based Filtering; TF-IDF;  K-Nearest Neighbor; Cosine Similarity. AbstrakKabupaten Probolinggo memiliki potensi destinasi wisata yang beragam, namun penyebaran informasi wisata yang belum optimal menyulitkan wisatawan dalam menentukan tujuan perjalanan. Penelitian ini bertujuan mengembangkan sistem rekomendasi destinasi wisata untuk membantu pengguna memperoleh rekomendasi sesuai preferensi. Sistem dikembangkan menggunakan metode Content-Based Filtering (CBF) dengan pembobotan TF-IDF dan cosine similarity serta algoritma K-Nearest Neighbor (KNN) berbasis cosine distance. Proses sistem meliputi pengumpulan data destinasi wisata, ekstraksi dan pembobotan fitur teks, perhitungan tingkat kemiripan, serta penyajian rekomendasi destinasi utama dan destinasi serupa. Pengujian sistem menggunakan metrik precision dan recall menunjukkan bahwa metode content-based filtering (CBF) dan K-Nearest Neighbor (KNN) menghasilkan nilai precision masing-masing sebesar 93,75% dan 96,00%, dengan nilai recall keduanya mencapai 100%, sehingga sistem mampu memberikan rekomendasi destinasi wisata yang relevan.Kata kunci: Sistem Rekomendasi; Pariwisata Probolinggo; Content-Based Filtering; TF-IDF;  K-Nearest Neighbor; Cosine Similarity.
Analisis Quality of Service pada Implementasi Video Call Berbasis SIP dengan Monitoring Zabbix pada Jaringan WLAN Avrian, Daniel Okta; Lase, Kristian Juri Damai; Maedjaja, Febe
Progresif: Jurnal Ilmiah Komputer Vol 22, No 1 (2026): Januari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i1.3528

Abstract

The development of IP-based communication technology has made video call services an efficient solution for real-time communication. This study analyzes the Quality of Service (QoS) performance of SIP-based video call implementation using the Zabbix monitoring system on a Wireless Local Area Network (WLAN). An experimental method was applied by implementing an Issabel PBX SIP server and conducting performance tests under five bandwidth variations, namely 2.5, 5, 10, 15, and 20 Mbps. Each test scenario was conducted for 15 minutes with a monitoring interval of 30 seconds. The evaluated QoS parameters include throughput, delay, jitter, and packet loss, which were assessed based on the TIPHON standard. The results show that higher bandwidth provides better QoS performance, while bandwidth limitation leads to performance degradation, particularly in packet loss and service stability. The evaluation also indicates that a minimum bandwidth of 5 Mbps is required to maintain acceptable video call quality on WLAN networks. Furthermore, this study demonstrates that Zabbix is effective as a continuous monitoring system capable of providing real-time and historical QoS data to support performance analysis and network evaluation.Kata kunci: Quality of Service; Session Initiation Protocol; Video call; Zabbix; WLAN. 

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