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All Journal bit-Tech Indonesian Journal of Electrical Engineering and Computer Science Jurnal Informatika Dan Tekonologi Komputer (JITEK) Jurnal Informatika Dan Tekonologi Komputer
Afina Lina Nurlaili
Universitas Pembangunan Nasional Veteran Jawa Timur
Computer Science & IT Control & Systems Engineering Education Other

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Journal : bit-Tech

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Implementation of the Weighted Product Method for Determining Poor Households Alya Izzah Zalfa Rihadah Ramadhani Nirwana Putri; Afina Lina Nurlaili; Muhammad Muharrom Al Haromainy
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3214

Abstract

Many village-level poverty programs still depend on manual deliberation, which is slow to audit and difficult to reproduce across localities. This study addresses that gap by delivering an end-to-end, transparent implementation of the Weighted Product (WP) method for ranking poor households in Prunggahan Kulon, Tuban Regency. We assess whether a clearly specified WP pipeline complete with documented polarity (benefit/cost), normalized weights, and run logs can convert heterogeneous village records into reproducible preferences suitable for operational targeting. Household data supplied by the village and the Social Office were coded on a 0–1 scale for eight agreed criteria; expenditure (C2) was treated as a cost while others were benefits. Equal weights were used in this initial deployment for clarity and explainability. The method was implemented in a Laravel-based system that records bases, signed exponents, the multiplicative score , and normalized preferences . A five-household subset (A1–A5) is reported for illustration, with the full system supporting larger lists. The computation yielded a clear ordering (A4 > A1 > A2 > A3 > A5). The multiplicative rule preserved penalties for critical shortfalls and prevented strong indicators from masking severe deprivations, while the software artifacts ensured traceability from inputs to final . The dataset comprised 491 households encoded across eight criteria, with one cost criterion and seven benefits. Compared with prior WP applications, our contribution is an end-to-end, district-ready pipeline with explicit polarity, documented weights, and preserved run logs enabling third-party replication. This design measurably improves transparency and reproducibility for local poverty targeting.
Comparative Analysis of Memory and Render Performance: BLoC vs Provider on Low-End Devices Muhammad Albert Nur Agathon; Afina Lina Nurlaili; Muhammad Muharrom Al Haromainy
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3268

Abstract

Mobile application performance is a critical determinant of user retention, yet optimization strategies for low-end hardware remain underexplored in the Flutter ecosystem. The choice of state management, specifically between Provider and BLoC, is a pivotal architectural decision affecting resource efficiency, particularly when integrated with Clean Architecture. Addressing the scarcity of empirical studies on constrained hardware, this research quantitatively compares the performance of these two libraries on a Vivo 1719 device running Android 7. Two identical Al-Qur'an applications were developed to facilitate a controlled experiment, isolating state management as the single variable. Performance metrics, including Resident Set Size (RSS), Garbage Collection (GC) frequency, and Frame Janks, were measured using Flutter DevTools during intensive scrolling and data loading scenarios. The results demonstrate that BLoC significantly outperforms Provider on low-end specifications. In the heaviest scenario, BLoC recorded lower peak memory usage (201.88 MB) compared to Provider (221.64 MB) and triggered 33% fewer GC events. Furthermore, BLoC reduced frame janks by 40% (15 janks vs. 21 janks). From a software engineering perspective, these findings indicate that BLoC's stream-based, event-driven architecture offers superior resource isolation compared to Provider's listener propagation mechanism, which tends to induce higher garbage collection overhead. Consequently, this study recommends BLoC as the preferred strategy for deployments targeting emerging markets, offering a worthwhile trade-off between development complexity and runtime stability to ensure broader digital inclusivity.
Comparison of Batch Size Values in MobileNetV2 for Stroke Classification Using CT Scan Images Ajeng Listya Devani; Anggraini Puspita Sari; Afina Lina Nurlaili; Nurul Hidajati
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3301

Abstract

Stroke is still one of the world's leading causes of death and permanent disability, necessitating a quick and precise diagnosis in order to choose the best course of treatment.  The purpose of this study is to examine how different batch size configurations affect the MobileNetV2 architecture's ability to classify stroke types from CT-scan brain pictures. The dataset comprises three categories Normal, Ischemic, and Bleeding sourced from Kaggle and RSUD Haji, East Java Province. The strategy to transfer learning was used utilizing pretrained ImageNet weights, with the network fine-tuned for stroke classification tasks. Experimental testing was conducted using three batch size configurations: 16, 32, and 64, while maintaining consistent hyperparameters for other training components. Among the assessment measures were accuracy, macro F1-score, and AUC (macro) to measure performance comprehensively. The results revealed that a batch size of 16 achieved the highest overall performance, with an accuracy of 96.14%, a macro F1-score of 96.15%, and an AUC of 99.62%, outperforming larger batch configurations. These findings indicate that smaller batch sizes enhance model generalisation and improve gradient update dynamics, enabling the CNN to better capture subtle patterns within CT-scan images. Thus, our study finds that the best trade-off between convergence speed and batch size is 16., model generalisation, and diagnostic accuracy, demonstrating the effectiveness of the MobileNetV2 architecture for automated stroke detection based on CT-scan imaging
Optimizing Plantation Production Prediction Using Category Boosting with Random Search and Walk-Forward Validation Faishal Fernando Hutama; Eva Yulia Puspaningrum; Afina Lina Nurlaili
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3354

Abstract

The plantation subsector is a cornerstone of the national economy, yet its productivity is increasingly volatile due to climate change. Predicting production yields remains challenging as traditional models often fail to capture complex nonlinear temporal dependencies and seasonal cycles. This study aims to improve the prediction accuracy of five major plantation commodities, namely palm oil, rubber, coffee, tea, and sugarcane, by optimizing the Category Boosting (CatBoost) algorithm. The analysis uses monthly data from 2009 to 2024, combining official production and land statistics from the Central Bureau of Statistics (BPS) with national temperature and rainfall records from the Meteorology, Climatology, and Geophysics Agency (BMKG) to ensure transparency. Unlike standard approaches that rely on default parameters and random data splitting, this research applies a rigorous optimization pipeline. Random Search is used for hyperparameter tuning, supported by lag features to capture short term dynamics and sinusoidal transformations to represent seasonal cycles. A Walk Forward Validation technique with an expanding window is employed to prevent look ahead bias and ensure realistic evaluation. The optimized model significantly outperforms the baseline. Sugarcane (R² 0.95) and Coffee (R² 0.97) show excellent accuracy, while Palm Oil improves markedly (R² 0.80) as more historical patterns are learned. Rubber and Tea remain difficult to predict, indicating insufficient explanatory features rather than model limitations. The study concludes that combining hyperparameter optimization with temporal feature engineering enables CatBoost to effectively model agricultural time series data and provides a solid foundation for strategic production planning.
ARAS Method for Ranking Vocational High School Students Achmad Andrian Maulana; Muhammad Muharrom Al Haromainy; Afina Lina Nurlaili
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3369

Abstract

Student performance assessment is a crucial component in strengthening the quality of vocational education. At SMK Muhammadiyah 2 Jogoroto, Kab. Jombang, the evaluation process is still conducted manually and relies primarily on report card scores, leading to subjectivity, inconsistency, and a limited representation of students’ competencies. This study develops a Decision Support System (DSS) by integrating the Rank Order Centroid (ROC) weighting technique and the Additive Ratio Assessment (ARAS) method to provide a clearer and more systematic multicriteria evaluation framework. The analysis involves ten student alternatives evaluated using six criteria: average report card score, attitude, absenteeism, extracurricular activities, achievements, and industrial internship performance. ROC is applied to generate proportional criterion weights based on ranked priority, while ARAS is used to execute the core computational stages, including normalization of each criterion, application of weighted values, calculation of the optimal function score (Si), and determination of utility values (Ui) to rank student performance. The results indicate that the system yields consistent outcomes, with Nikmatuz achieving the highest utility value of 2.65224526 and identified as the top-performing student. These findings show that combining ROC and ARAS enhances assessment accuracy, reduces evaluator bias, and improves transparency in the ranking process. Beyond this case study, the proposed model demonstrates potential for broader application in vocational institutions seeking structured, data-driven mechanisms to evaluate academic and non-academic competencies more comprehensively.
Implementation of the WASPAS Method for Selecting an Optimal Project Leader Erwin Erdiyanto; M. Muharrom Al Haromainy; Afina Lina Nurlaili
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3373

Abstract

Selecting an optimal project leader is a critical organizational process that strongly influences project performance, coordination efficiency, and overall operational outcomes. Poor selection decisions may increase delays, inefficiencies, and reduced team productivity. To address these challenges, this study applies the Weighted Aggregated Sum Product Assessment (WASPAS) method to evaluate eight project leader candidates using five leadership-related criteria: leadership ability, communication skills, professional experience, technical expertise, and problem-solving capability. All candidate scores were compiled into a decision matrix and normalized to ensure comparability across criteria. WASPAS was implemented through its dual-component structure, combining the additive Weighted Sum Model (WSM) and the multiplicative Weighted Product Model (WPM) to generate comprehensive preference values (Qi). This hybrid mechanism enables the method to capture both absolute and proportional differences in candidate competencies. The results show that WASPAS successfully ranked all candidates and identified the strongest performer, with the highest Qi value recorded at 3.00 and the lowest at 2.09, demonstrating a clear distinction in overall competency levels. The top-ranked candidate, Sintya Dwi Rachmawati, consistently scored high across all criteria, confirming the method’s capability to differentiate performance profiles effectively. These findings highlight the methodological precision of WASPAS in supporting structured leadership selection and underscore its potential to enhance fairness and analytical rigor in organizational decision-making. Overall, the study concludes that WASPAS is a reliable and practical multi-criteria decision-making technique suitable for leadership-oriented evaluations within diverse organizational contexts.
Co-Authors Achmad Andrian Maulana Ajeng Listya Devani Alya Izzah Zalfa Rihadah Ramadhani Nirwana Putri Anggraini Puspita Sari Aulia Saharani Erwin Erdiyanto Eva Yulia Puspaningrum Faishal Fernando Hutama I Dewa Gde Satria Pramana Erlangga Indah Rahmawti Utami M. Muharrom Al Haromainy Muhammad Albert Nur Agathon Muhammad Muharrom Al Haromainy Muhsin Nurul Hidajati Sugiarto, Sugiarto