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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 1,401 Documents
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Deep Learning-Based Weather Prediction Model Using BMKG Big Data for Rice Planting Season Optimization in Indonesia Maradona Jonas Simanullang; Elsya Sabrina Asmita Simorangkir; Herry Daniel Marpaung; Yudisa Halawa; Tria Adelia Putri Br Gurusinga; Frans Mikael Sinaga
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16703

Abstract

This study develops and evaluates a deep learning-based weather prediction model built on BMKG (Indonesian Meteorological, Climatological, and Geophysical Agency) data, with the aim of informing rice planting season optimization in Deli Serdang Regency, North Sumatra, Indonesia. Two recurrent architectures, Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM), were compared for daily rainfall prediction using 788 observations collected between January 2024 and March 2026, split chronologically into training, validation, and testing subsets. The LSTM model achieved a Root Mean Square Error (RMSE) of 24.42 mm, a Mean Absolute Error (MAE) of 15.20 mm, and an R² of 0.0420, while the BiLSTM model achieved a lower RMSE of 23.64 mm, a lower MAE of 14.89 mm, and a lower R² of 0.0233. Although BiLSTM produced marginally smaller errors, LSTM explained a slightly larger share of rainfall variability, and both R² values remained close to zero, indicating that neither model captured the full variability of the rainfall series. Because of its higher R², the LSTM-based forecasts were translated into an indicative cropping-calendar classification of wet, normal, and dry months as a preliminary basis for advancing, maintaining, or delaying the onset of the rice planting season in Deli Serdang Regency. Given the limited explanatory power of both models and the absence of baseline forecasts, repeated training, and time-series cross-validation in this study, these planting-season indications should be read as exploratory and complementary to, rather than a replacement for, the operational cropping calendar issued by BMKG and the Ministry of Agriculture. Future research should incorporate longer historical records, additional meteorological predictors, baseline models, and rigorous validation to develop more reliable, data-driven rice planting season decision support for Indonesia.
Explainable AI-Based Hybrid LightGBM–LSTM for Banking Fraud Detection Rafif Ramadhan Al Yarda; Fauziah Fauziah
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16711

Abstract

Banking fraud detection faces changing transaction behaviors, class imbalance, and the need to interpret the outputs of complex models. This study develops a Fraud Detection and Analysis System (FDAS) as a Decision Support System in the form of a prototype that integrates tabular and temporal modeling, imbalance handling, operational validation, and post-prediction interpretation. A quantitative experimental approach was employed using a synthetic transaction dataset constructed based on the characteristics of observed fraud and normal patterns, comprising 50 accounts, consisting of 25 fraud accounts and 25 normal accounts, covering the period from January to December 2025. Account-level splitting was used to prevent historical information leakage across accounts, while TPH-SMOTE and KHOI-SMOTE were applied only to the training set. LightGBM and LSTM were trained independently to represent tabular and temporal data, respectively, and their probabilities were subsequently combined through weighted decision-level late-fusion, with SOP serving as the operational validation layer. SHAP was employed as a post-hoc explanation to support interpretability, traceability, and human oversight. Across 1,000 paired evaluation units, the final configuration achieved an Accuracy of 97.4%, Precision of 96.8%, Recall of 96.2%, F1-score of 96.5%, and ROC-AUC of 0.984. Comparison across configurations indicated that the highest performance was achieved by the integrated configuration within the experimental environment used. FDAS demonstrates end-to-end integration to support fraud investigation through recommendations and interpretive information, while the final decision remains with the analyst. The use of synthetic data, 50 accounts, and a prototype status limits direct generalization to operational banking populations and production readiness.
Sentiment Analysis of Flip Application Reviews Using DBSCAN Outlier Removal and SVM Hidayatul Ichwan; Fajar Mahardika; Ratih; Riki Aldi Pari
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16720

Abstract

Flip is an Indonesian financial technology application offering free interbank transfers, whose Google Play Store review column has become a large unstructured record of user experience that a development team cannot read manually. Such reviews are noisy, containing slang, repeated characters, and very short comments that behave as outliers. This study measures whether removing DBSCAN-detected outliers from the training data improves a support vector machine that assigns Flip reviews to three rating-derived categories. From 20,000 collected reviews, 15,241 remained after duplicate removal and preprocessing. The corpus was split once into stratified training and test partitions before any fitting, so that TF-IDF, the truncated singular value decomposition and DBSCAN were fitted on the training partition alone and both scenarios were evaluated on one identical held-out test set of 3,049 documents, repeated over five random seeds. Because the k-distance curve shows no pronounced knee, eps was fixed at the ninetieth percentile of that distribution, and eps and minPts were varied over a grid of nine combinations. Accuracy was 0.8023 without filtering and 0.8033 with it, a paired difference of +0.0010 with a 95 percent confidence interval of [-0.0017, +0.0038] and p = 0.347; macro F1-score moved from 0.6390 to 0.6402. No combination in the grid produced a distinguishable improvement. Of forty flagged documents inspected manually, none was genuine noise, and 91 percent of the flagged documents belonged to the majority positive class. The results give no evidence that density-based filtering benefits short-text classification.
Integrated Enterprise Architecture for Coffee Shop Supply Chain and Customer Interaction Using TOGAF ADM Sanky Yunliahni; Handri Santoso; Erick Dazki; Januponsa Dio Firizqi; Richardus Eko Indrajit
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16726

Abstract

Although the coffee shop sector continues to expand quickly, enterprise architecture research in this domain has generally treated the customer-facing point of sale and the upstream sourcing of coffee as two separate concerns, so that supply chain and customer interaction are rarely represented together. This study builds an integrated enterprise architecture model for a coffee shop enterprise that brings supply chain operations and customer interaction into a single design spanning three product lines: coffee beverage, non-coffee beverage, and food and snack sales. The study is conceptual and design-oriented; the enterprise modelled is a representative reference case reconstructed from operational patterns reported in earlier studies of independent coffee shops, not an empirically observed firm. The work follows the phases of The Open Group Architecture Framework Architecture Development Method, from the Preliminary Phase to Migration Planning, with ArchiMate as the modelling notation and the Business Model Canvas as the expression of the business logic. The findings show that the Architecture Vision phase connects three stakeholder groups and their strategic drivers to specific realizing components, namely integrated point-of-sale management, customer service management, and smart inventory management, while the Business Architecture phase shows that all three product lines share a recurring procurement-inventory-preparation-sales pattern and rely on a common supplier and point-of-sale application layer instead of separate systems. A preliminary analytical self-assessment of coverage and traceability indicates internal completeness, whereas expert validation remains future work. The model offers a reusable reference architecture for the digital transformation of small and medium coffee shop enterprises.
Enterprise Architecture for Multi-Sided E-Commerce Marketplaces: A Completeness-Driven TOGAF-Zachman Approach Stephen Edlin; Handri Santoso; Erick Dazki; Januponsa Dio Firizqi; Richardus Eko Indrajit
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16735

Abstract

Indonesian micro, small, and medium enterprises (MSMEs) are moving into digital commerce, but the move remains scattered. National statistics show that 94.76% of e-commerce businesses sell through instant-messaging applications and only 17.23% through a marketplace platform, while 48.03% still arrange delivery themselves and cash remains the dominant settlement method. Multi-sided marketplace platforms can consolidate this activity, but designing them is architecturally demanding. Existing enterprise architecture (EA) studies also seldom verify whether every fundamental descriptive dimension of the enterprise has been addressed. This study proposes a completeness-driven EA for a multi-sided e-commerce marketplace. It integrates the TOGAF Architecture Development Method (ADM) as the design process, ArchiMate 3 as the modeling language, and the Zachman Framework’s 5W1H ontology (What, How, Where, Who, When, Why) as an explicit completeness lens. Within a Design Science Research approach, the artifact is developed across TOGAF ADM Phases A to D and evaluated through architectural consistency checking, gap analysis, and a structured completeness assessment that maps every model element onto a perspective-by-aspect matrix under a stated population rule. Of the 24 cells assessed, 20 are fully populated, 3 are only partially populated, and 1 is weak. The What, How, Where, Who, and Why aspects are answered at all four perspectives, while the temporal When aspect accounts for every cell that falls short. The study contributes a reusable blueprint for marketplaces that advance SME digitalization, an escrow mechanism modeled end-to-end including its dispute path and regulatory driver, and a replicable procedure for assessing architectural completeness.
Hierarchical Coverage Optimization of 5G New Radio Non-Standalone in Tourism Corridors Afrizal Yuhanef; Muhammad Putra Pamungkas; Amran Paso Salmeno; Fikri Adi Pratama
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16738

Abstract

A hierarchical coverage-optimization strategy is proposed for 5G New Radio Non-Standalone (NR NSA) networks in hilly tourism corridors with transient, high-mobility users. A drive-test campaign (7,522 measurement points, G-NetTrack Pro) was conducted on the Telkomsel 5G NR NSA network in the Jam Gadang corridor, Bukittinggi, West Sumatra, Indonesia, and benchmarked against three radio-planning scenarios simulated in Atoll. Three interventions were evaluated in order of increasing capital cost: antenna tilt and azimuth tuning, small-cell densification, and the addition of new macro-sites. Drive-test results show 97.55% of RSRP samples in the very-good band (≥ −85 dBm), confirming throughput — not coverage — as the dominant constraint. Against the simulation baseline (77.30% very-good RSRP, 70.55% very-good SINR), antenna tuning alone raises RSRP to 84.50% very-good and reduces the poor-band SINR from 2.03% to 0.06% at zero hardware cost. Small-cell densification achieves the strongest throughput gain (99.00% very-good TPUT-DL), while new macro-site addition achieves the strongest coverage gain (92.20% very-good RSRP, 0.00% poor RSRP). These results substantiate a cost-ordered hierarchical planning sequence transferable to similar tourism heritage corridors in emerging markets.
When Touch Cannot Be Digitized: ArchiMate Enterprise Architecture for Omnichannel Wellness Services Heni Rohmawati; Handri Santoso; Januponsa Dio Firizqi; Erick Dazki; Richardus Eko Indrajit
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16739

Abstract

Wellness providers such as massage studios are digitalizing their booking, membership, and home-visit channels, yet the service they sell still happens through a therapist's hands, a condition that omnichannel services and phygital services research, grown largely out of retailing, rarely translates into architectural specification. This study designs an enterprise architecture for a micro-enterprise wellness services provider in Banten, Indonesia, that plans three channels, namely regular on-site massage, monthly membership, and home service. Following design science research and TOGAF ADM phases A to D, six ArchiMate models were developed and then analysed through a digitizability lens that classifies every touchpoint as fully digitizable, digitally mediated, or irreducibly physical. Five of the nine normalized activities are fully digitizable and every one of them is information handling, two are digitally mediated, and the two irreducibly physical activities, the therapist's travel and the massage itself, appear in none of the six models. That omission is precisely what makes a distinct family of frictions architecturally invisible. Twelve anticipated frictions are therefore separated into boundary frictions, which exist because the service is physical, and generic digital frictions. The resulting target architecture surrounds the physical activity with an information envelope of identity assurance, shared availability, arrival status, and safety tracing. What cannot be digitized can still be surrounded by information that reduces uncertainty. The artifact is evaluated analytically and ex ante, no implementation exists yet, and systematic measurement is the necessary next step.
Implementation of Soft Voting Ensemble Learning for Early Detection of Type 2 Diabetes Mellitus Carles; Osi Draini; Sabtria Winda Sari; Nu'man; M. Khairul Anam
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16740

Abstract

Type 2 Diabetes Mellitus is a chronic disease that requires early detection to minimize the risk of complications. This study aims to develop a machine learning model for the early detection of Type 2 Diabetes Mellitus using an ensemble Soft Voting approach. The proposed model combines three algorithms, namely LightGBM, Random Forest, and Support Vector Machine. The dataset used consists of patient health attributes, including pregnancies, glucose level, blood pressure, skin thickness, insulin level, body mass index, diabetes pedigree function, and age. The preprocessing stage was conducted to prepare the data before the training process, including data balancing so that each class contained the same number of records, namely 500 diabetes data and 500 non-diabetes data. The evaluation results show that the model achieved an accuracy of 80%, with precision, recall, and f1-score of 0.80 for both classes. In addition, the Area Under the Curve value of 0.89 indicates that the model has a very good capability in distinguishing between diabetes and non-diabetes classes. The system was also implemented in the form of a Graphical User Interface to facilitate patient clinical data input and display risk detection results. Based on these results, the developed Soft Voting model can be used as a practical and sufficiently accurate tool for the early detection of Type 2 Diabetes Mellitus.
Comparative Study of TF-IDF and SBERT Feature Representations for Random Forest-Based Resume Classification Fathya Fathimah Azzahra; Roni Andarsyah; Mohamad Nurkamal Fauzan
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16761

Abstract

Manual resume screening in high-volume recruitment is time-consuming and prone to inconsistent evaluation, motivating automated screening using Machine Learning (ML) and Natural Language Processing (NLP). Prior comparisons of TF-IDF and Sentence-BERT (SBERT) often differ in classifiers or task formulations, limiting direct comparison of feature representations. This study compares TF-IDF and SBERT using an identical Random Forest classifier for resume-to-role classification across 32 role categories. The dataset contains 10,102 resume-job description pairs from a public Hugging Face corpus. Three sources of data leakage were identified and mitigated, followed by five-fold StratifiedGroupKFold cross-validation and Bonferroni-corrected paired statistical testing. TF-IDF Standard achieved a mean macro F1 of 0.9953, TF-IDF Enriched 0.9964, and SBERT 0.9816. The difference between the two TF-IDF configurations was not significant (p = 0.1367), whereas both significantly outperformed SBERT (p = 0.0021 and p = 0.0015). Component-wise ablation showed that resume-only input retained high performance (macro F1 up to 0.9977), while job-description-only input achieved at most 0.2339, indicating that resume content provides the primary predictive signal. Equalizing the input budget to 256 tokens also preserved the TF-IDF advantage. Role-mismatch results further showed no performance degradation when job descriptions were replaced with descriptions from unrelated roles. These findings indicate that TF-IDF provides strong lexical representations for resume-to-role classification under the evaluated protocol, while highlighting the importance of leakage auditing and controlled input conditions.
The Development, Trends, and Challenges of Smart Mosque Research: A Systematic Literature Review 2021–2026 Jamrud Aminuddin; Safiq Safiq; Kartika Sari
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16768

Abstract

Smart Mosque research has expanded from energy efficiency and building automation toward wider digital services involving artificial intelligence, Internet of Things, data management, operational security, and inclusivity. Objective: This study maps the development, trends, and challenges of Smart Mosque research published between 2021 and 2026 using a Context-Technology-Impact framework. Methods: A PRISMA 2020-guided systematic literature review was conducted using reproducible PubMed and Crossref searches and second-reviewer validation. Records were screened against mosque/masjid or Hajj/Umrah technology relevance, assessed using an eight-item quality-appraisal instrument, and classified by evidence-access status. Results: The search produced 1,664 exported records and 870 records after deduplication. After staged screening, second-reviewer validation, and excluded-sample consensus, 72 records were retained, consisting of 10 full-text confirmed records and 62 limited-access/provisional records. The evidence shows four dominant directions: artificial intelligence for operational security and Hajj/Umrah crowd monitoring, energy and sustainability systems, Internet of Things and automation, and digital mosque-management platforms. Conclusion: Smart Mosque research is developing into a broader digital-operational ecosystem, but the evidence remains methodologically uneven because many records were available only at abstract or limited-access level and 2026 data were partial up to August 2026. Future studies should prioritize full-text-verifiable empirical research, multi-site evaluation, system reliability, user-centered assessment, privacy and data governance, and inclusive digital services.

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