Jurnal Studi Multidisiplin Ilmu

Smartphone Selection Decision Support System for Online Motorcycle Taxi Drivers Using the SMART Method

Muhammad Annash Ash Shidiq — 2025-01-05

Purpose: Online motorcycle taxi (ojek online) drivers in Indonesia require smartphones that support intensive all-day outdoor use, including strong battery life, performance, screen visibility, and network stability. This study aims to develop a web-based Decision Support System (DSS) using the SMART (Simple Multi Attribute Rating Technique) method to recommend the most suitable smartphone based on occupation-specific criteria.
Methodology: A design and development research approach was used. Ten criteria were identified through observation and interviews, including price, battery capacity, processor, storage, RAM, screen brightness, network technology, display size, and camera quality. Four Oppo smartphones (A12, A16K, A52, and F11) were evaluated. Criteria weights were assigned based on driver priorities, then normalized. Utility values were calculated using the SMART formula, and final scores were obtained from weighted utility aggregation.
Results: The SMART analysis ranked Oppo F11 first (Nta = 70.5), followed by Oppo A52 (59.2), Oppo A12 (45.8), and Oppo A16K (37.9). The F11 achieved the highest score due to its balanced affordability, strong chipset performance, large storage, and adequate RAM capacity.
Conclusions: The SMART-based DSS effectively supports smartphone selection for ojek online drivers by integrating multicriteria decision-making into a structured system. The model demonstrates reliable ranking results for the evaluated alternatives.
Limitations: The study is limited to four Oppo models and relies on expert-based weighting, which may not fully represent broader driver preferences.
Contributions: This study provides a replicable SMART-based DSS framework for occupational smartphone selection in the gig economy, particularly for improving decision accuracy in driver-oriented technology recommendations.

K-Nearest Neighbors Based Matic Motorcycle Damage Prediction System Web Application Preventive Maintenance Bengkel Sahabat Motor

Anggi Wijaya — 2025-01-10

Purpose: This study develops a web-based matic motorcycle damage prediction system using the K-Nearest Neighbors (KNN) algorithm at Bengkel Sahabat Motor to support early damage detection, preventive maintenance, and cost reduction.

Methodology: A quantitative approach with waterfall System Development Life Cycle (SDLC) was used. Data were collected through observation, interviews, and workshop records. The system was built using Personal Home Page (PHP), html, Cascading Style Sheets (CSS), JavaScript, and MySQL. KNN with Euclidean distance and K=3 was applied, using a three-level symptom scale. System design used Unified Modeling Language (UML) and validation was conducted through black box testing.

Results: The system accurately classifies motorcycle damage, with test outputs correctly identifying "Engine Overheating" based on nearest neighbor distances. Black box testing achieved 100% acceptance across 143 test items, categorized as “Very Good.” Diagnosis time decreased from 30 to 10 minutes per case.

Conclusions: The KNN-based system effectively automates motorcycle damage classification and improves diagnostic efficiency.

Limitations: The study is limited to a single workshop, small dataset, no IoT integration, and lacks formal accuracy metrics.

Contributions: This study provides a practical machine learningbased predictive maintenance system for motorcycle workshops, offering a replicable framework for digital diagnostics in the automotive service sector.

Web-Based Decision Support System for Prosperous Family Classification Using the Analytical Hierarchy Process

Andino Maseleno — 2025-01-07

Purpose: This study designs and implements a web-based Decision Support System (DSS) for classifying prosperous families in Dusun Cibanban, Desa Gerning, Tegineneng District, Pesawaran Regency, Indonesia, applying the Analytical Hierarchy Process (AHP). The work addresses the inefficiency and subjectivity of manual welfare assessment routines used by local administrators.
Methodology: A design science research approach guided development, following the waterfall model through requirements analysis, design, implementation, testing, and deployment. AHP derived priority weights from pairwise comparisons among welfare criteria adapted from national family welfare standards, implemented using Personal Home Page (PHP) with CodeIgniter, JavaScript, and MySQL.
Results: The system computed AHP priority weights for every household head across six welfare criteria and produced a ranked classification for 102 households. Black-box testing confirmed all primary modules operated without defects. The household head Marsidi obtained the highest composite score, 0.1736, with a criteria consistency ratio of 0.071, within Saaty's acceptable threshold.
Conclusions: The system replaces a slow, subjective manual procedure with a faster, more transparent classification mechanism accessible through an ordinary browser.
Limitations: The evaluation was confined to a single sub-village with 102 household heads, criteria weights rested on expert elicitation rather than empirical validation, and concurrency performance under production loads was not assessed.
Contributions: The study offers a documented, replicable community-level AHP-DSS implementation that local administrators elsewhere in Indonesia can adapt for data-driven welfare targeting.

Effects of Work Ability and Motivation on Employee Productivity in North Jakarta Manufacturing Firm Study

Ade Solihin — 2025-01-08

Purpose: This study examines how work ability and work motivation shape employee work productivity at PT Gautama Indah Perkasa, a manufacturing firm in North Jakarta, Indonesia, a setting where fluctuating output has pushed management to look for evidence-based grounds for its human resource decisions.
Methodology: A quantitative, descriptive survey was conducted among the firm's 50 permanent employees. A saturated sampling approach produced 36 usable responses. Work ability, work motivation, and work productivity were each measured with Likert-scaled instruments whose validity and reliability were confirmed through corrected item-total correlation and Cronbach's Alpha, after which classical assumption tests preceded a multiple linear regression run in SPSS 23.
Results: All twenty-three items were valid and every construct was reliable. The fitted equation, Y = 6.128 + 0.287X1 + 0.343X2, shows that work ability carries a significant partial effect (B = 0.287, t = 2.971, p = .006) while work motivation's partial effect, though positive, falls short of significance (B = 0.343, t = 1.680, p = .102). Jointly, the two predictors explain 40.4 percent of productivity variance and are simultaneously significant (F = 11.175, p = .000).

Conclusions: Work ability stands out as the dependable, individually significant driver of productivity in this factory, whereas motivation contributes mainly through its combined effect with ability.
Limitations: A single-firm, cross-sectional design with only 36 respondents restricts generalizability and causal claims.
Contributions: The study offers Indonesian manufacturing HR practitioners a concrete basis for prioritizing competency-building investment over purely motivational programs.