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Nahrun Hartono
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System Information and Computer Technology (SYNCTECH)
Published by CV. SUBALTREN INTI MEDIA
ISSN : -     EISSN : 30892724     DOI : -
Core Subject : Science,
Computer Science & IT
System Information and Computer Technology (SYNCTECH) is a peer-reviewed, open-access journal published by the CV Subaltren Inti Media. It has been published online since 2024. SYNCTECH publishes original research findings and high-quality scientific articles that present cutting-edge approaches, including methods, techniques, tools, implementations, and applications. The journal serves as an archival resource for researchers, scientists, and engineers involved in all aspects of information technology, computer science, computer engineering, information systems, and software engineering. SYNCTECH is registered with BRIN under e-ISSN: 3089-2724. SYNCTECH is published twice a year, in February and July. Every manuscript submitted is reviewed by expert reviewers through a double-blind peer-review process. Manuscripts may be submitted in Bahasa Indonesia or English. SYNCTECH also accepts submissions for its “Selected Papers” section. These are special articles published in the nearest edition and must meet the following criteria: Written in English, and Include at least one co-author affiliated with an institution outside Indonesia. If your paper meets these requirements, please contact our editorial representative to secure a slot in the “Selected Papers” section.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 20 Documents
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Desain dan Evaluasi Prototipe Jaringan Sensor Nirkabel untuk Monitoring Lahan Persawahan di Kabupaten Gowa Andi Muhammad Nur Hidayat; A. Zulfan Donangsyah; Aldin Ihsan; Muh. Rihan
System Information and Computer Technology (SYNCTECH) Vol. 2 No. 1 (2026): February
Publisher : Subaltren Inti Media

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Abstract

Monitoring agricultural land is a crucial element in increasing productivity and food security. Gowa Regency has extensive and scattered rice fields, necessitating an efficient and affordable automated monitoring system. This study designed and evaluated a wireless sensor network (WSN) prototype based on Arduino and XBee modules to monitor land conditions in real time, including temperature, air humidity, and soil moisture. The evaluation included network coverage, connection stability, sensor accuracy, and the system's ability to support environmental data-driven decision-making. The results showed that the prototype was capable of transmitting data in real time with low deviation, demonstrating the potential of WSN as an agricultural monitoring tool in Gowa Regency
Implementasi Mesh Wi-Fi untuk Mengatasi Deadzone di Rumah Andi Muhammad Nur Hidayat; Muhammad Ryaas Rahmat; Andi Nur Rizqah Aulia Renggala
System Information and Computer Technology (SYNCTECH) Vol. 2 No. 1 (2026): February
Publisher : Subaltren Inti Media

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Abstract

including within household environments. However, the presence of areas without signal coverage, or dead zones, remains a serious obstacle in the use of conventional router-based wireless networks. This study aims to evaluate the effectiveness of implementing a Mesh Wi-Fi network in addressing dead zone issues in residential settings, taking into account room configuration, wall obstructions, and potential interference. The research was conducted using an experimental approach by deploying Wi-Fi 6–based Mesh Wi-Fi devices in a two-story house that previously experienced connectivity issues. Data were collected through measurements of signal strength, download and upload speeds, latency, and network stability across three critical areas of the house. The results show a significant improvement, with an average increase of 26.05 dBm in signal strength and more than a 500 percent increase in download speeds after the implementation of Mesh Wi-Fi compared to a single-router network. In addition,the variation in signal quality across locations decreased, indicating a more even distribution of network coverage. These findings demonstrate that Mesh Wi-Fi is an effective and practical solution for improving network quality in homes with complex building structures, and it is capable of overcoming the limitations of traditional solutions such as signal boosters or additional routers. Therefore, the implementation of Mesh Wi-Fi is recommended as an alternative for providing stable internet connectivity in modern households.
Pengelompokkan Tindakan Kriminalitas di Indonesia dengan K-Medoids Menggunakan Algoritma Artificial Bee Colony Adinda Pratiwi Musa; Novianita Achmad; Salmun K. Nasib
System Information and Computer Technology (SYNCTECH) Vol. 2 No. 1 (2026): February
Publisher : Subaltren Inti Media

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Abstract

This research aims to classify regional police forces in Indonesia based on the level and characteristics of criminal activity by applying the K-Medoids method optimized using the Artificial Bee Colony (ABC) algorithm. This grouping is intended to identify the dominant crime patterns in each region and evaluate the effectiveness of the methods used in producing representative clusters. The analysis results show that the K-Medoids-ABC method produces three main clusters, with the distribution of each consisting of 7 regional police departments in cluster 1, 5 regional police departments in cluster 2, and 21 regional police departments in cluster 3. Cluster validation using the Silhouette Index (SI) yielded a value of 0.387, indicating that the clustering results fall into the weak structure category, meaning the cluster structure is formed but with weak separation (Weak Separation). Cluster 1 shows a moderate and relatively even crime rate, Cluster 2 is dominated by crimes against life and crimes of fraud, embezzlement, and corruption, while Cluster 3 shows low values across all variables, with the lowest values for violent property crimes and drug-related crimes. This cluster reflects regions with relatively safe conditions, as evidenced by very low crime rates. These differences in characteristics between clusters reflect the diversity of factors causing crime in each region and have important implications for formulating more contextual and targeted crime prevention strategies.
Integrasi Data Penelitian dan Pengabdian Dosen untuk Monitoring Berbasis Data Warehouse Yunita Amalia; Nahrun Hartono; Asrul Azhari Muin
System Information and Computer Technology (SYNCTECH) Vol. 2 No. 1 (2026): February
Publisher : Subaltren Inti Media

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Abstract

The management of research and community service data of lecturers in higher education institutions requires an integrated system to support effective monitoring and reporting processes. At UIN Alauddin Makassar, research and community service data had previously been stored separately in Microsoft Excel files managed by different staff members, which made data processing, retrieval, and comprehensive analysis difficult. This study employed a quantitative approach with stages consisting of requirements analysis, system design, data warehouse implementation, and system testing. Data integration was carried out through the Extract, Transform, and Load process into a structured centralized data storage. System testing was conducted using the Black Box Testing method to ensure that all system functions operated according to user requirements. The results showed that the implementation of the data warehouse successfully integrated research and community service data in a structured manner, reduced data duplication, and improved efficiency in data retrieval and report generation. The developed system also provided monitoring reports in the form of a web-based dashboard that was informative and easy to understand. Therefore, the implementation of this data warehouse was able to assist the Institute for Research and Community Service (LP2M) in managing and monitoring lecturers’ research and community service activities more effectively and optimally.
Implementasi AI Object Recognition Real-Time Menggunakan TensorFlow.js dan Integrasi WhatsApp Agung Setyadi; Adhy Rizaldy; Atika Reski; Muhammad Al Fauzan Bobihu
System Information and Computer Technology (SYNCTECH) Vol. 2 No. 1 (2026): February
Publisher : Subaltren Inti Media

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Abstract

Real-time monitoring system development frequently encounters accessibility challenges when deployed on mid-to-low-end hardware. This study documents the experimental process of developing a web-based AI object recognition system utilizing TensorFlow.js and the COCO-SSD model. Prior research employing COCO-SSD has demonstrated suboptimal performance, with response times exceeding 33 ms. During the development phase, custom logic incorporating overlap mechanisms and cooldown features was implemented to address limitations inherent in basic object detection when recognizing human-object interactions. To optimize real-time performance, this logic was applied at the application level rather than within the AI model itself, leveraging the latest deep learning methodologies proven to outperform YOLO. Using a private training dataset comprising limited facial and indoor object images, the system successfully visualizes bounding boxes and sends instant WhatsApp alerts via whatsapp-web.js. The methodology adheres to an integrated web-based object detection workflow. Experimental results demonstrate a responsive system with latency below 30 ms, meeting real-time performance standards. This paper concludes that the JavaScript-based AI stack, combined with spatial logic, effectively provides a functional solution for automatic activity recognition.
Rancang Bangun Sistem Informasi Pemesanan Jasa Kompon Mobil Berbasis Web Menggunakan Metode Waterfall (Studi Kasus: Kompon Mobil Gendrux Kartasura) Stevanus Handriyanto; Handoko Handoko
System Information and Computer Technology (SYNCTECH) Vol. 2 No. 2 (2026): July
Publisher : Subaltern Inti Media

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Abstract

The development of information technology has provided convenience in various fields, including vehicle repair services. One highly demanded service is mobile car polishing (kompon) to restore scratched or lightly damaged vehicle body surfaces. However, conventional booking processes often lead to scheduling conflicts, data recording errors, and a lack of accurate service status transparency. This study aims to design and develop a web-based car polishing service booking system. The primary advantage and innovation of this system, which differentiates it from previous studies, is the integration of a real-time scheduling algorithm that automatically prevents double-booking and features specific status tracking tailored for on-demand service models. The system was developed using the Waterfall SDLC (Software Development Life Cycle) method, with data collection conducted through observation, interviews, and literature studies. The application was built using the PHP programming language and a MySQL database. System evaluation was carried out using Blackbox Testing to ensure all functionalities operate without logical errors, and User Acceptance Testing (UAT) to measure end-user satisfaction. The results indicate that the developed system effectively facilitates online bookings, prevents scheduling overlaps, and assists service providers in structured order management. Furthermore, the testing confirms that all features function flawlessly, and the application has achieved a high level of user acceptance.
Klasifikasi Diabetes Menggunakan Algoritma K-Nearest Neighbor dengan Preprocessing dan Min-Max Scaling pada Pima Indians Diabetes Dataset Nurdilla Nurdilla; Roberto Kaban
System Information and Computer Technology (SYNCTECH) Vol. 2 No. 2 (2026): July
Publisher : Subaltern Inti Media

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Abstract

Diabetes mellitus is a chronic metabolic disease characterized by high blood glucose levels and has the potential to cause various complications if not detected early. The use of machine learning technology is increasingly developing in the health sector because it can help the process of analyzing and classifying diseases based on patient data. This study aims to apply the K-Nearest Neighbor (KNN) algorithm to classify diabetes using the Pima Indians Diabetes Dataset. The dataset used consists of 768 patient data with 8 predictor attributes and 1 target attribute. The research stages include data cleaning and improvement through preprocessing, data normalization using the Min-Max Scaling method, dividing the dataset into training data and testing data with a ratio of 80:20, and the application of the KNN algorithm with a K value of 5. Model performance evaluation was carried out using a Confusion Matrix which produces Accuracy, Precision, Recall, and F1-Score values. Based on the test results, the model obtained Accuracy of 74.68%, Precision of 66.00%, Recall of 60.00%, and F1-Score of 62.86%. These results demonstrate that the KNN algorithm is capable of classifying diabetes data with fairly good performance based on available health attributes. This research is expected to serve as a reference in the development of machine learning-based decision support systems to aid in the identification of diabetes.
Analisis Pengaruh Rekayasa Prompt AI Terhadap Penafsiran Ayat Mutasyabihat: Studi Kasus QS. Thaha Ayat 5 di ChatGPT Hastuti Hastuti; Zul Faris Ali; Muhammad Yuswane Aulia
System Information and Computer Technology (SYNCTECH) Vol. 2 No. 2 (2026): July
Publisher : Subaltern Inti Media

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The development of artificial intelligence (AI) has influenced how society accesses and understands religious information, including Qur’anic interpretation studies. ChatGPT is one of the technologies used to support the understanding of religious texts, but its ability to handle prompt manipulation in interpreting mutashabihat verses requires further study. This research aims to analyze the influence of prompt variations on ChatGPT responses in interpreting QS. Thaha: 5 and evaluate its consistency based on tafsir standards. This study uses a qualitative library research approach with experimental testing through three types of prompts: neutral, persona, and manipulative. ChatGPT outputs were analyzed using content analysis and compared with Tafsir Ibn Kathir and Tafsir Al-Misbah. The results show that ChatGPT maintains interpretation boundaries according to tafsir parameters, including when facing manipulative prompts that lead to incorrect theological interpretations. However, the system has limitations because it does not possess religious understanding and moral judgment. Its responses are generated from previously learned textual data and language patterns, not from belief or religious understanding. This study shows that AI can support tafsir studies but still requires human supervision and verification.
Penerapan IndoBERT untuk Pencarian Semantik Tafsir Al-Qur'an Berdasarkan Tafsir Al-Misbah Taslim Sultan; Faisal Akib; Muhammad Hasrul Hasanuddin
System Information and Computer Technology (SYNCTECH) Vol. 2 No. 2 (2026): July
Publisher : Subaltern Inti Media

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Abstract

Conventional Qur'anic exegesis retrieval systems that rely on keyword matching often return less relevant results because they are unable to capture the semantic meaning and context of users' queries. This study aims to develop a semantic search system for Tafsir Al-Misbah using the IndoBERT model within the CRISP-DM framework. The dataset consists of 6,236 Indonesian-language tafsir summaries processed through data cleaning, case folding, normalization, and tokenization. The IndoBERT model was then fine-tuned to generate semantic representations of user queries and document contents, while FAISS was employed to accelerate retrieval using cosine similarity. System performance was evaluated using Mean Average Precision (mAP), Mean Reciprocal Rank (MRR), and Normalized Discounted Cumulative Gain (nDCG). The evaluation results achieved Top-10 scores of 0.2128 for mAP, 0.2681 for MRR, and 0.2792 for nDCG, while the Top-50 scores reached 0.2301, 0.2803, and 0.3481, respectively. These results indicate adequate retrieval performance in the domain of Qur'anic exegesis and demonstrate that the proposed semantic search approach based on IndoBERT and FAISS is capable of providing contextually relevant retrieval results for users' queries.
A Hybrid Wavelet-CUSUM Approach for Anomaly Detection in Denial of Service Attacks Andi Muhammad Nur Hidayat; Antamil Antamil; Avilah Ramadhani
System Information and Computer Technology (SYNCTECH) Vol. 2 No. 2 (2026): July
Publisher : Subaltern Inti Media

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Denial of Service (DoS) attacks pose a significant threat to network availability by overwhelming target systems with abnormal traffic volumes. This research proposes a hybrid detection method combining Discrete Wavelet Transform (DWT) and Cumulative Sum Control Chart (CUSUM) to detect multi-stage DoS attacks with improved sensitivity and reduced false alarm rates. Synthetic network traffic was generated using Poisson distribution to simulate normal conditions and three attack scenarios of varying intensity. The Haar wavelet decomposed the traffic signal, separating anomalous high-frequency components from normal patterns. CUSUM was then applied to the detail coefficients to detect cumulative shifts indicative of sustained attack activity. Results demonstrate that the standalone wavelet method with static thresholding achieved a detection rate of 39.64%, while CUSUM-enhanced detection reached 81.98%, representing a 106.8% improvement. The hybrid approach maintained a false alarm rate comparable to wavelet-only detection (30.63% vs 35.14%) while significantly improving sensitivity. Detection delay averaged 3 samples (44.67 seconds), enabling early attack identification suitable for real-time intrusion response systems. The proposed method offers a lightweight, training-free alternative to machine learning approaches, making it suitable for resource-constrained network environments

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