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Sinkron : Jurnal dan Penelitian Teknik Informatika
Published by Politeknik Ganesha Medan
ISSN : 2541044X     EISSN : 25412019     DOI : 10.33395/sinkron.v8i3.12656
Core Subject : Science,
Computer Science & IT
Scope of SinkrOns Scientific Discussion 1. Machine Learning 2. Cryptography 3. Steganography 4. Digital Image Processing 5. Networking 6. Security 7. Algorithm and Programming 8. Computer Vision 9. Troubleshooting 10. Internet and E-Commerce 11. Artificial Intelligence 12. Data Mining 13. Artificial Neural Network 14. Fuzzy Logic 15. Robotic
Arjuna Subject : Ilmu Komputer (semua kategori) - Visi Komputer dan Pengenalan Pola
Articles 1,361 Documents
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Comparative Evaluation of Feature-Driven Development and Scrum in Public Facility Booking Systems Wahdini Wahdini; Hilyah Magdalena
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16079

Abstract

The Parittiga Sub-District Office still relies on manual procedures for multipurpose building borrowing, resulting in limited schedule transparency, double booking vulnerabilities, and delayed confirmations. However, a critical research gap exists: no quantitative comparison between Feature-Driven Development (FDD) and Scrum has been conducted for public facility booking systems. To address this, a web-based booking system was developed using FDD and empirically compared with Scrum through a comparative experimental design. Using identical two-developer teams, both methods were evaluated across five metrics: development time per feature, functional error rate (black-box testing), developer understanding (1-5 scale), usability (SUS), and end-to-end borrowing efficiency. Findings demonstrate that FDD outperforms Scrum with 50% fewer functional errors (2 vs 4), higher developer understanding (4.3 vs 3.8), and better usability (84.2 - Excellent vs 71.6 - Good), despite requiring 2 additional hours per feature (12 vs 10). Moreover, the FDD-based system accelerates the complete borrowing process by 63% (4 vs 11 minutes). This research contributes empirical evidence to the agile methodology literature and offers a structured, replicable evaluation framework for practitioners selecting development methods for stable, documentation-intensive public service environments.
A Comparative MCDM Framework Integrating AHP, SAW and TOPSIS for Robust Public Sector Selection velisia kartika; Hilyah Magdalena
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16082

Abstract

The selection of top employees at DINPMP2KUKM in Bangka Regency faces challenges including subjective evaluation, a lack of clear criteria, and difficulties in assessing employee performance across different fields, which may lead to perceptions of unfairness and decreased work motivation. This study aims to apply a comparative multi-criteria decision-making framework combining the Analytic Hierarchy Process (AHP), Simple Additive Weighting (SAW), and the Technique of Order Preference by Similarity to Ideal Solution (TOPSIS). A mixed-methods approach was employed, involving five respondents (four division heads and one human resources sub-division head) and three employee alternatives. Data were collected through structured interviews and paired comparison questionnaires. AHP was used to determine criterion weights, while SAW and TOPSIS were applied to rank alternatives, followed by sensitivity analysis to test ranking robustness. Unlike prior studies that generally used only a single MCDM method without robustness testing, this study validates ranking consistency across three different MCDM methods with sensitivity-based robustness testing. The results indicate that cooperation (29.2%) is the dominant criterion, followed by performance (22.6%), discipline and innovation (16.8% each), and integrity (14.6%), with a consistency ratio of 0.05 indicating consistent evaluations. All three methods produced identical rankings, with Employee 3 selected as the top employee (43%), excelling in four out of five criteria. Sensitivity analysis confirmed that Employee 3 remained at the top when the weight of cooperation was altered by ±20%, demonstrating decision robustness. Given the limitations in the number of respondents and alternatives, these findings suggest that the combination of AHP, SAW, and TOPSIS with sensitivity analysis can produce more consistent employee selection outcomes compared to single-method approaches. Further research is recommended to develop a web-based decision support system with a larger number of respondents and alternatives.
CTRI Framework: Integrating COBIT 4.1 Maturity, Risk Priority, and Transition to COBIT 2019 Kasmala Kasmala; Hilyah Magdalena
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16083

Abstract

This study proposes the CTRI (COBIT Transition and Risk Integration) framework, a novel methodological integration for IT governance evaluation in SMEs. Unlike prior COBIT 4.1 studies that only report maturity gaps, the CTRI framework addresses three critical gaps: (1) lack of actionable recommendations, (2) absence of risk and feasibility considerations, and (3) no systematic bridge from COBIT 4.1 to COBIT 2019. The CTRI framework consists of three integrated layers. Layer 1 quantifies maturity gaps across six COBIT 4.1 domains (PO2, AI2, AI6, DS5, DS11, ME1). Layer 2 introduces the Risk–Feasibility Priority Matrix (RFPM) which calculates a Priority Action Score (PAS) = Risk Impact × Implementation Feasibility, where risk impact is high (3), medium (2), or low (1), and feasibility is easy (3), moderate (2), or difficult (1). Recommendations with PAS ≥ 6 are top priority. Layer 3 provides explicit transition mapping from each COBIT 4.1 recommendation to COBIT 2019 governance objectives and design factors. Applied to a sales application at PT Ciequ (Indonesian SME), data were collected via observation, interviews with three key informants, and documentation review. Findings reveal an average maturity level of 2.45, with largest gaps in AI2 (1.23) and DS5 (0.89). The RFPM prioritizes AI2 (PAS=9), DS5 (PAS=6), and AI6 (PAS=6) as top actions. A three-phase transition roadmap (Stabilize → Standardize → Monitor) to COBIT 2019 is provided, with APO13 (security) and APO05 (portfolio) as priority objectives. This study contributes a reusable, risk-aware, transition-forward methodology that bridges legacy and modern IT governance frameworks for resource-constrained SMEs.
Lean Software Development for Reducing Bureaucratic Waste in Internship Administration Nasir Zumali; Hilyah Magdalena
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16084

Abstract

Government internship administration in Indonesia remains heavily reliant on manual procedures, causing substantial operational waste. While prior studies have applied agile methods to digitize similar workflows, none have quantitatively measured bureaucratic waste reduction using inferential statistical validation. This study contributes a novel integration of Lean Software Development with Value Stream Mapping and inferential statistics to empirically validate waste elimination in public sector internship administration. A mixed-method explanatory sequential design was employed. First, Value Stream Mapping quantified lead time and cycle time on 30 paired internship applications. A web-based system was then developed using Lean Software Development principles. Functional validity was verified through Black Box testing with 50 test cases, and user satisfaction was assessed using the System Usability Scale on 30 respondents. The Shapiro-Wilk normality test confirmed non-normal data distribution, justifying the Wilcoxon Signed-Rank Test for hypothesis testing. Results demonstrated complete functional validity, a 97.5% reduction in lead time from 72.5 to 1.8 hours (Z=-4.782, p<0.001, r=0.87), a 90.9% reduction in cycle time from 25.4 to 2.3 minutes (Z=-4.801, p<0.001, r=0.88), and an excellent System Usability Scale score of 82.5. These findings provide empirical evidence that integrating Lean principles with robust statistical validation offers a replicable framework for quantitatively measuring bureaucratic efficiency gains, advancing beyond descriptive case studies in digital governance research.
Comparative Study of Johnson and Bellman-Ford for Shortest Path in OpenFlow SDN Afriza Tri Rizki; Funny Farady Coastera; Ernawati Ernawati
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16086

Abstract

Software-Defined Networking (SDN) provides a highly programmable architecture specifically by decoupling the control plane from the data plane. The efficiency of the controller in mapping topologies and responding to link failures depends heavily on the shortest-path algorithm used. While previous studies have evaluated algorithms like Bellman-Ford in small scale SDN, empirical comparisons with hybrid algorithms like Johnson in medium-scale dense topologies remain significantly limited. This study aims to provide an empirical comparative evaluation of Johnson and Bellman-Ford algorithms on OpenFlow 1.3 using the RYU controller, analyzing scalability across ring (sparse) and full-mesh (dense) topologies from 10 to 50 nodes. The research methodology relies on experimental emulation using Mininet to test convergence time, throughput, and recovery time during dynamic link failures. The results indicate that in sparse ring topologies, both algorithms achieve similar convergence under 0,06 seconds. However, in dense 50 node full-mesh networks containing 2.450 links, Bellman-Ford demonstrates a faster average convergence of 37,93 seconds compared to Johnson's 47,44 seconds, primarily due to the absence of graph reweighting overhead, despite exhibiting higher variance. Both algorithms maintained stable throughput, and while recovery times generally met the near carrier-grade standard, some scenarios in dense networks reached 60 milliseconds, slightly exceeding the 50 ms threshold. This study evaluates recovery during single link failure scenarios. In conclusion, Bellman-Ford is highly recommended for dense data center infrastructures, while Johnson is optimal for sparse networks requiring instant route recovery.
Academic Chatbot for Campus Information Services Using Retrieval-Augmented Generation Haddad Alwi Yafie; Achmad Udin Zailani; Widang Muttaqin; Muhammad Sheva Atallah Daffansyah; Muhammad Rafi Ramzi
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16088

Abstract

University service centers handle many repetitive queries about academic schedules, registration, and policies stored in internal documents. Manual lookup is inefficient, and answers given by staff can be inconsistent. Rule-based chatbots only handle limited question patterns, while large language models are hard to update and may produce unsupported answers (hallucinations). This research designs an academic chatbot that combines document retrieval with answer generation so that each answer remains traceable to its source. The system extracts text from campus documents, segments it, encodes it using a multilingual embedding model, and stores it in a vector index for context retrieval. A response is generated through an instruction template that confines the output to the retrieved information and includes page references. Evaluation followed a mixed-method design: a quantitative layer measured retrieval quality (Precision@5, Recall@5) and generation quality using the four RAGAS sub-metrics (faithfulness, answer_relevancy, context_precision, context_recall) on a 100-question test set, while a qualitative layer applied thematic analysis to open-ended user comments. Statistical testing used McNemar's test for accuracy and a paired bootstrap (10,000 resamples) for retrieval metrics; 95% confidence intervals are reported. Results: the proposed RAG system achieved 84% answer accuracy (95% CI 76–90%), Precision@5 = 0.80 and Recall@5 = 0.72, with a System Usability Scale (SUS) score of 78 and a Net Promoter Score (NPS) of +32 from 30 participants. Differences in accuracy versus the lexical and LLM-only baselines were statistically significant (McNemar p < 0.05). The system offers a replicable instantiation of RAG for transparent, citation-backed campus information services in Indonesian.
DTUF-PS: A Design Thinking-Based Usability Framework for Android Harvest Monitoring in Oil Palm Plantations Novita Novita; Hilyah Magdalena
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16092

Abstract

One of Indonesia’s major plantation commodities is oil palm, but the still-manual documentation process hinders operational efficiency and real-time monitoring. Although the use of Design Thinking in mobile app development is growing, critical gaps remain, such as the lack of research that systematically integrates plantation-specific parameters—for example, the Harvest Density Index, Average Bunch Weight, and rotation cycle—into a usability evaluation framework tailored to the cognitive load of field workers. This study aims to develop and evaluate a prototype of an Android-based harvest monitoring system using a UI/UX approach and the Design Thinking method. The study involved 30 respondents, consisting of 5 division assistants and 25 field workers, and utilized the System Usability Scale (SUS), User Experience Questionnaire (UEQ), and time efficiency measurements. Empirical findings show an average SUS score of 80 (SD=6.1), categorized as “Good” (70th percentile), UEQ Perspicuity of 1.79 (85th percentile), “Very Good,” and a 69.9% increase in time efficiency from 600.6 to 180.7 seconds, paired t-test p<0.001, Cohen’s d=4.03, with instrument reliability confirmed by Cronbach’s α ranging from 0.73 to 0.87). The main contribution of this study is the Design Thinking-Based Usability Framework for Plantation Systems (DTUF-PS), which introduces three transferable constructs: Domain Artifact-Driven Empathize (DADE), Parameter-Embedded Ideation (PEI), and the Triadic Usability Evaluation Model (TUEM). This framework is proposed as an initial transferable model that requires further validation in other plantation contexts, such as rubber, cocoa, and coffee. The research results indicate that the Design Thinking method is effective in meeting user needs in agricultural monitoring.
INTEGRATED PERFORMANCE AND IOT-BASED RELIABILITY ASSESSMENT OF OFF-GRID PV SYSTEMS Joni Eka Candra; Mhd Adi Setiawan Aritonang; Muhammad Nazwan
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16102

Abstract

Island regions often face unreliable electricity supply due to limited grid infrastructure, resulting in high dependence on diesel generators with significant economic and environmental drawbacks. This study aims to evaluate the performance and reliability of an Internet of Things (IoT)-integrated off-grid solar photovoltaic (PV) system implemented in a small island setting. A 400 Wp off-grid PV system was deployed on Pulau Puteri, Indonesia, and equipped with an IoT-based monitoring platform to enable real-time acquisition of operational data. The methodology includes system design, installation, and continuous monitoring over a 30-day period with 5-minute data intervals. System performance was assessed using Performance Ratio (PR) and Capacity Factor (CF). The system produced 1.6–2.1 kWh/day with PR values of 0.75–0.82 and CF of 16.7%–21.9%, indicating efficient and stable operation under tropical conditions. IoT monitoring enabled continuous data acquisition and early anomaly detection, improving operational reliability. The results confirm that the system meets international performance benchmarks and provides a feasible solution for decentralized energy systems in remote islands. The study contributes an integrated framework combining real-time IoT monitoring and standardized performance evaluation using field data.  
Classification of Paddy as Visual Anomaly in Rice Piles Using MobileNetV2-Based Convolutional Neural Network Barokah Saadah; Tri Aristi Saputri
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16129

Abstract

Rice is a strategic food commodity whose quality is assessed based on the visual appearance of the grains, including the presence of unhusked rice as an undesirable element in piles of milled rice. Manual inspection is subjective, time-consuming, and prone to errors, necessitating a more objective automated approach. To address this issue, this study applies a MobileNetV2-based Convolutional Neural Network with transfer learning to classify unhusked grains as visual anomalies in rice piles into two classes: normal rice and anomalous grains. In terms of methodology, the dataset consists of 1,000 self-acquired images stratified into three groups with a 70:15:15 ratio. Image preprocessing was performed via background removal using the rembg library and random background simulation with five background color variations. Training was conducted in two phases: Phase 1 (transfer learning with a frozen base model) and Phase 2 (fine-tuning by opening the last 30 layers of the base model). The evaluation results on the test data showed an accuracy of 90.67%, a macro precision of 0.9213, a macro recall of 0.9067, and a macro F1-score of 0.9058. The false positive rate across all tests was 0. Phase 1 was selected as the best model because it produced more stable performance compared to Phase 2. Grad-CAM visualizations confirmed that the model focuses its attention on the visual features of the objects, not background patterns. These findings demonstrate that a combination of preprocessing, transfer learning, and data augmentation is effective for binary image classification when dealing with limited datasets.
Multi-Metric Evaluation of Machine Learning Algorithms for Diabetes Prediction Using Feature Importance and ROC Analysis Fendi Setiawan; Tri Sugihartono
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16133

Abstract

Diabetes mellitus has become a major global health threat, and many undiagnosed cases remain undetected due to some limitations of the conventional diagnostic methods. Despite the promising results of machine learning (ML) for early diabetes diagnosis, the majority of the current research assessing algorithms either uses insufficient metrics or does not follow a consistent assessment approach. This paper addresses that gap by utilising an integrated evaluation framework. The framework includes feature importance analysis, Pearson correlation assessment, confusion matrix decomposition, and ROC-AUC comparison. It applies this framework to the Pima Indians Diabetes Dataset (mde) and four popular ML classification algorithms: Naive Bayes, Decision Tree, Random Forest, and Logistic Regression. The most significant predictors, according to our feature analysis, were glucose (27.6%), body mass index (16.0%), age (12.7%), and diabetes pedigree function (12.7%). Among the classifiers, Random Forest exhibited the greatest accuracy (76.0%) and precision (68.1%), Naive Bayes the best recall (64.8%), and Logistic Regression the highest AUC-ROC (82.3%). For patients at high risk, the models' virtual projections across all three risk profiles were in agreement. Model selection should be determined by the unique clinical screening aim, since these findings suggest that there is no one better universal method. Random Forest and Logistic Regression are the most promising for assisting in preliminary diabetes prediction, although further validation on diversity datasets is needed prior to clinical deployment.

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