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Jalan Gatot Subroto No 47 LK I, Desa/Kelurahan Tanjunggading, Kec. Kedamaian, Kota Bandar Lampung, Provinsi Lampung, Indonesia
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CHAIN: Journal of Computer Technology, Computer Engineering and Informatics
Published by Tech Cart Press
ISSN : 29642450     EISSN : 29642485     DOI : https://doi.org/10.58602/chain
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
CHAIN: Journal of Computer Technology, Computer Engineering and Informatics is a peer-review journal focusing on Computer Technology, Computer Engineering and Informatics. CHAIN invites academics and researchers who do original research in computer technology, computer engineering and informatics. CHAIN: Journal of Computer Technology, Computer Engineering and Informatics are published by Tech Cart Press in January, April, July, and October every year. CHAIN: Journal of Computer Technology, Computer Engineering and Informatics accept articles in Bahasa Indonesia and English.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 77 Documents
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Objective Approach in Supplier Selection: Integration of RECA Weighting and Combinative Distance-based Assessment Method Setiawansyah Setiawansyah; Iryanto Chandra
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 4 No. 3 (2026): Volume 4 Number 3 July 2026 (ONLINE FIRST)
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v4i3.264

Abstract

Supplier selection is a strategic decision that directly affects operational efficiency and supply chain performance. This study aims to propose a multi-criteria decision-making approach to evaluate and rank suppliers objectively based on multiple performance indicators. The evaluation is conducted using five main criteria, namely price, quality, delivery, responsiveness, and capacity and flexibility. A total of nine supplier alternatives were assessed, and a quantitative decision model was applied to aggregate the performance of each alternative into a final score and ranking. The results indicate that the proposed approach is capable of clearly distinguishing supplier performance, as reflected in the significant differences in final scores across alternatives. The ranking results show that PT Cipta Solusi Persada achieved the first position with a final score of 0.5171, followed by PT Karya Nusantara with a score of 0.4626 in the second position, and PT Prima Logistik Indonesia with a score of 0.3922 in the third position. These findings demonstrate that suppliers with balanced performance across all criteria tend to achieve higher rankings. The study also highlights that suppliers with lower rankings generally exhibit structural weaknesses in key criteria, suggesting the need for performance improvement or strategic reconsideration.
Rekomendasi Penginapan di Liwa Lampung Barat Berbasis Data Google Maps Menggunakan Aspect-Based Sentiment Analysis dan CRITIC-CoCoSo Badiwibowo Atim, Sandi; Citra, Erin Eka
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 4 No. 1 (2026): Volume 4 Number 1 January 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v4i1.265

Abstract

Pemilihan penginapan merupakan salah satu Pemilihan penginapan merupakan salah satu permasalahan yang sering dihadapi oleh pengunjung luar daerah ketika berkunjung ke Liwa, Lampung Barat. Informasi penginapan yang tersedia di Google Maps menyediakan data penting seperti rating, jumlah ulasan, lokasi, dan pengalaman pengguna, namun informasi tersebut belum secara langsung menghasilkan rekomendasi yang terukur. Penelitian ini bertujuan untuk membangun model Sistem Pendukung Keputusan rekomendasi penginapan di Liwa Lampung Barat berbasis data Google Maps menggunakan metode CRITIC dan CoCoSo, serta dirancang untuk dikembangkan dengan Aspect-Based Sentiment Analysis pada ulasan pengguna. Data yang digunakan terdiri dari 10 alternatif penginapan, yaitu Astama Boutique Hotel, Hotel ONO Syariah, Rosa Losmen Ono, RedDoorz Syariah near Kebun Raya Liwa, Robbani Edotel Liwa Syariah, Sunrise Hill Petik Bintang, Sarirasa Hotel Liwa, Hotel Sahabat Utama, Hotel Permata Liwa, dan KADAKA Villa & Cottage Liwa. Kriteria yang digunakan dalam perhitungan awal meliputi rating Google Maps, jumlah ulasan yang ditransformasi logaritmik, dan jarak ke pusat Liwa. Hasil pembobotan CRITIC menunjukkan bahwa jumlah ulasan memperoleh bobot tertinggi sebesar 0,445, diikuti jarak sebesar 0,300 dan rating sebesar 0,255. Hasil perangkingan CoCoSo menunjukkan bahwa KADAKA Villa & Cottage Liwa memperoleh peringkat pertama dengan nilai 2,524, diikuti Sunrise Hill Petik Bintang sebesar 2,355 dan Rosa Losmen Ono sebesar 2,277. Hasil penelitian menunjukkan bahwa integrasi data Google Maps dan metode CRITIC-CoCoSo dapat menghasilkan rekomendasi penginapan yang lebih objektif dibandingkan hanya menggunakan rating
Sistem Irigasi Drip Otomatis Menggunakan Metode Extreme Programming Berbasis Internet of Things Stephano Caesar Wenston Ngangi; Aditya Lapu Kalua; Jelly Ribka Danaly Lumingkewas; Eric Alfonsius
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 4 No. 3 (2026): Volume 4 Number 3 July 2026 (ONLINE FIRST)
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v4i3.272

Abstract

Pertumbuhan teknologi yang pesat, terutama di bidang Internet of Things (IoT), telah membuka peluang besar untuk mengatasi berbagai masalah dalam sektor pertanian, termasuk irigasi. Salah satu masalah utama yang dihadapi oleh petani adalah inefisiensi dalam penggunaan air, terutama pada sistem irigasi konvensional yang seringkali kurang akurat dalam mendistribusikan air secara tepat sesuai kebutuhan tanaman. Sistem manual juga memerlukan tenaga dan waktu yang signifikan, yang berpotensi menghambat produktivitas.Untuk mengatasi masalah ini, penelitian ini mengusulkan pengembangan sistem irigasi tetes otomatis berbasis IoT menggunakan metode Extreme Programming (XP). Solusi ini melibatkan penggunaan sensor kelembaban tanah dan mikrokontroler yang terhubung ke jaringan internet, memungkinkan pemantauan dan pengendalian irigasi secara real-time. Melalui penerapan metode XP, sistem ini dirancang secara iteratif dengan pendekatan yang fleksibel, memudahkan penyesuaian terhadap kebutuhan pengguna dan kondisi lapangan. Hasil dari pengembangan menunjukkan bahwa sistem irigasi tetes otomatis ini mampu meningkatkan efisiensi penggunaan air hingga 30% dibandingkan sistem manual. Sistem berhasil diimplementasikan dan diuji pada skala hidroponik, dengan hasil pertumbuhan tanaman yang lebih baik dan pengurangan pemborosan air. Antarmuka sistem berbasis web juga memungkinkan pengguna untuk memantau kondisi tanah dan mengatur waktu irigasi secara jarak jauh. Penerapan sistem irigasi tetes otomatis berbasis IoT dengan metode XP memberikan solusi efektif untuk mengatasi inefisiensi irigasi di sektor pertanian. Sistem ini tidak hanya memudahkan pengelolaan air, tetapi juga mendukung keberlanjutan dengan cara yang hemat energi dan sumber daya.
Applying Analytical Hierarchy Process in a Decision Support System for Study Program Recommendation Aldyth Najma Rova Marthin; Mahardika Inra Takaendengan; Marline Sofiana Paendong
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 4 No. 3 (2026): Volume 4 Number 3 July 2026 (ONLINE FIRST)
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v4i3.274

Abstract

Choosing a study program is a critical academic decision because it affects students' learning direction, skill development, and career readiness. This study designs, implements, and evaluates a web-based Decision Support System for study program recommendation using the Analytical Hierarchy Process. The model uses four criteria: interest and talent, technology-related hobby, academic score, and job prospects. The research used teacher criteria data before web implementation and student alternative data after the system was implemented. Teacher matrices were screened using the Consistency Ratio requirement, and the valid matrix produced criteria weights of 0.436 for interest and talent, 0.320 for job prospects, 0.192 for technology-related hobby, and 0.053 for academic score. The system was developed with Python and Flask, then evaluated using Black Box Testing and User Acceptance Testing. The main scenario produced Informatics Engineering as the first recommendation with a score of 0.4880 or 49 percent. Across 24 post-implementation student responses, Informatics Engineering was also the most frequent top recommendation, appearing in 10 responses, followed by Mathematics in 9 responses. However, only 16 of 96 student alternative matrices met the CR threshold, which indicates that automatic consistency validation is needed. Black Box Testing confirmed that all tested core functions worked as expected, and UAT produced an acceptance percentage of 81 percent. These results show that the proposed system can provide systematic and usable recommendation support, while consistency control remains the main technical improvement needed.
Sistem Deteksi Penyakit Gigi Berbasis Deep Learning Menggunakan YOLOv8 dan ResNet-18 Bagas Aditya; Rully Pramudita
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 4 No. 3 (2026): Volume 4 Number 3 July 2026 (ONLINE FIRST)
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v4i3.280

Abstract

Penyakit gigi dan mulut menjadi salah satu masalah kesehatan yang paling umum di Indonesia, dengan prevalensi mencapai 57,6% populasi menurut Survei Kesehatan Indonesia. Proses diagnosis konvensional masih sangat bergantung pada pemeriksaan visual manual oleh dokter gigi, yang memiliki keterbatasan dari segi waktu, subjektivitas, dan aksesibilitas. Penelitian ini mengembangkan sistem deteksi penyakit gigi berbasis deep learning yang mengintegrasikan arsitektur YOLOv8 untuk object detection dan ResNet-18 untuk image classification dalam sebuah pipeline ensemble. Sistem dirancang untuk mendeteksi enam jenis kelainan gigi: karies, karang gigi, radang gusi, hipodontia, sariawan, dan diskolorasi gigi dari foto kamera ponsel. Dataset yang digunakan berjumlah 11.957 citra yang dibagi menjadi 70% data latih, 15% validasi, dan 15% pengujian. Teknik weighted sampling diimplementasikan untuk menangani ketimpangan kelas dengan rasio 7,96x. Pelatihan ResNet-18 menggunakan optimizer Adam (learning rate 0,001) dengan fungsi kerugian CrossEntropyLoss berbobot kelas dinamis. Hasil evaluasi menunjukkan YOLOv8 mencapai mAP@50 sebesar 88,17%, sementara ResNet-18 memperoleh akurasi klasifikasi 92,25% dengan F1-Score 92,37%. Validasi statistik 5-Fold Cross Validation mengonfirmasi stabilitas ResNet-18 (Standar Deviasi = ±0,45%) dan YOLOv8 (Standar Deviasi = ±1,95%). Sistem ini diimplementasikan dalam aplikasi web menggunakan FastAPI dan Next.js pada GPU NVIDIA T4, dengan latensi end-to-end 2-4 detik, serta dilengkapi modul Grad-CAM untuk interpretabilitas prediksi.
Explainable Machine Learning for Network Intrusion Detection Using SHAP-Based Feature Interpretation Eka Wahyu Sholeha; Dery Yuswanto Jaya; Qorry Aina Fitroh
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 4 No. 3 (2026): Volume 4 Number 3 July 2026 (ONLINE FIRST)
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v4i3.283

Abstract

Network Intrusion Detection Systems (NIDS) play a crucial role in protecting computer networks from increasingly sophisticated cyberattacks. Although machine learning techniques have demonstrated high detection performance, many models operate as black-box systems, making it difficult for security analysts to understand the reasoning behind prediction outcomes. This study proposes an explainable machine learning framework for network intrusion detection using the Random Forest algorithm and SHAP (SHapley Additive exPlanations)-based feature interpretation. The CICIDS2017 Friday-WorkingHours-Afternoon-DDos dataset was utilized to evaluate the effectiveness of the proposed approach. Data preprocessing included data cleaning, handling missing values, label encoding, and dataset partitioning. The Random Forest classifier was trained and evaluated using Accuracy, Precision, Recall, and F1-Score metrics. Experimental results demonstrated excellent classification performance, achieving an accuracy of 99.9889%, precision of 99.9922%, recall of 99.9883%, and F1-score of 99.9902%. Furthermore, SHAP analysis was employed to improve model interpretability by identifying the contribution of individual features to intrusion detection decisions. The results revealed that Fwd Packet Length Max, Destination Port, Avg Fwd Segment Size, and Fwd Packet Length Mean were among the most influential features affecting classification outcomes. The integration of Random Forest and SHAP not only achieved highly accurate intrusion detection but also enhanced transparency and trustworthiness by providing meaningful explanations for model predictions. Therefore, the proposed framework offers an effective and interpretable solution for network intrusion detection in modern cybersecurity environments.
Analisis Keamanan Website SMK VIP Al-Huda Kebumen Menggunakan Metode Penetration Testing Execution Standard (PTES) Abdur Rahman Fadilah; Ghufron Zaida Muflih
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 4 No. 3 (2026): Volume 4 Number 3 July 2026 (ONLINE FIRST)
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v4i3.290

Abstract

The rapid growth of information technology and internet usage has increased cybersecurity threats to web-based applications. The website of SMK VIP Al Huda Kebumen, which functions as a digital information and service platform, has the potential to become a target of cyberattacks if security evaluations are not conducted regularly. This study aims to analyze the security level of the SMK VIP Al Huda Kebumen website using the Penetration Testing Execution Standard (PTES) method. PTES was chosen because it provides systematic testing stages including pre-engagement interaction, information gathering, threat modeling, vulnerability analysis, exploitation, post-exploitation, and reporting. The testing process utilized several tools such as Zenmap/Nmap, OWASP ZAP, SQLMap, DNS Scan, and Infoga. The results indicate that the website still has several potential security vulnerabilities, including open service ports, brute force attack risks on SSH services, credential theft risks on FTP services, and possible exploitation of cPanel services. In addition, the vulnerability analysis identified several low and medium risk vulnerabilities that could potentially be exploited by attackers. Although the system is protected by a firewall and uses Linux operating system with Apache web server, further improvements are still required through regular system updates, better encryption implementation, service access restrictions, and additional security policies. This research is expected to become a reference for improving school website security and preventing cyber threats in educational environments.

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