https://www.motivection.imeirs.org/index.php/motivection/issue/feedMOTIVECTION : Journal of Mechanical, Electrical and Industrial Engineering2026-08-07T07:48:17+00:00Wawan Purwantowawan5527@gmail.comOpen Journal Systemshttps://www.motivection.imeirs.org/index.php/motivection/article/view/583Geometric Redesign and Cut-and-Fill Volume Estimation of a Non-Compliant Mine Haul Road at Pit Banko Tengah A, South Sumatra2026-07-26T06:32:24+00:00Dwi Lestariputridwi222@gmail.comSyari Rahma Yantisyarirahma@ft.unp.ac.idTri Gamela Saldytrigamelasaldy@ft.unp.ac.idRangga Agung Pribadi Heriawanrangga_agung@ft.unp.ac.id<p>Mine haul roads must satisfy geometric standards to ensure safe and reliable heavy-equipment mobilization. This study evaluated the inactive Tesla haul road at Pit Banko Tengah A, South Sumatra, for reactivation using Komatsu HD-785 rigid dump trucks. Existing road geometry was assessed against operational standards, followed by 3D spatial redesign and cut-and-fill volume estimation in hilly terrain. The results showed that several straight segments require widening, with the largest correction reaching 7.93 m to meet the 24 m standard. Segment 5 also requires curve widening and superelevation correction, with actual superelevation of 0.064 m compared with the 1.223 m design requirement. Road grades remained within the 8% maximum limit. The redesign produced an excavation-dominant earthwork profile, consisting of 203.92 BCM of cut and 9.79 BCM of fill. These findings provide a practical basis for planning haul-road reactivation while aligning geometric compliance with earthwork requirements.</p>2026-07-26T05:00:49+00:00##submission.copyrightStatement##https://www.motivection.imeirs.org/index.php/motivection/article/view/608Comparative Exhaust-Gas Characteristics of Sugarcane Bagasse and Pineapple Waste Bioethanol Blends in a Fuel-Injected Motorcycle under a Common HHO-System Configuration2026-07-27T09:15:36+00:00Calvin M. P. Sinagavinnaega@gmail.comWawan Purwantowawan5527@ft.unp.ac.idM. Yasep Setiawanm.yasepsetiawan@ft.unp.ac.idAhmad Arifahmadarif@ft.unp.ac.id<p>Agricultural waste-derived bioethanol offers a renewable gasoline-blending option for spark-ignition engines. This study compared sugarcane bagasse- and pineapple waste-derived bioethanol blends in a fuel-injected Honda BeAT motorcycle operated under a common HHO-system configuration. Five fuel formulations—E0, E5-SC, E10-SC, E5-PW, and E10-PW—were tested at 1500, 4000, and 7000 rpm. CO, HC, CO₂, and O₂ were measured using a four-gas analyzer, while λ and AFR were reported by the analyzer. Results are presented as means from three replicate runs. E10-SC recorded the lowest mean CO concentration at 4000 rpm and the lowest mean HC concentration at 7000 rpm, whereas E10-PW recorded the lowest mean CO concentration at 7000 rpm. The formulation rankings varied with engine speed and measured parameter. Because HHO was not tested independently, its specific contribution could not be determined.</p>2026-07-27T09:10:07+00:00##submission.copyrightStatement##https://www.motivection.imeirs.org/index.php/motivection/article/view/555Task-Level Mapping of Electrical and Oil-Handling Risks during Transformer Oil Purification: A Job Safety Analysis at PT PLN Batam2026-07-27T09:41:37+00:00Nanda Putri Utaminandaputami@gmail.comHery IrwanHery04@gmail.com<p class="mAbstractEnglish">Transformer oil purification combines electrical isolation, auxiliary power, pressurized oil transfer, dielectric testing, and recommissioning, creating interconnected electrical and oil-handling hazards. This study mapped task-level risks and specified stage-specific controls for the purification workflow at PT PLN Batam using Job Safety Analysis. A single-site case study examined ten stages through observation, 30 maintenance interviews, and operational safety-document review. Hazards were classified retrospectively using a 5 × 5 likelihood–severity matrix. Seven stages were rated high risk and three medium risk. Purification-unit connection received the highest score (15) because electrical and oil-transfer interfaces converge at this stage. Proposed controls emphasize verified isolation, lockout/tagout, competent authorization, pressure-rated connections, containment, spill readiness, and independent restoration checks. Under assumed full implementation and verification, the profile is projected to shift to seven medium-risk and three low-risk stages. The study provides a transparent task-level control map while distinguishing projected residual risk from measured outcomes.</p>2026-07-27T00:00:00+00:00##submission.copyrightStatement##https://www.motivection.imeirs.org/index.php/motivection/article/view/542ChatGPT as Learning Support among Mechatronics Engineering Technology Students: A Descriptive Survey2026-08-05T07:55:20+00:00Yuniarmelinda Ikha Meru Brahmantyyuniarmelindaimb@gmail.comArif Ainur Rafiqarifainur.2020@student.uny.ac.idFeta Kukuh Pambudifetakukuh@gmail.comYoana G ita Pradnya Lengariyoanalengari014@gmail.com<p style="margin: 0cm; text-align: justify;">Generative artificial intelligence is increasingly used in higher education, yet evidence from applied engineering programs remains limited. This descriptive survey examined self-reported ChatGPT use across six academic dimensions among 51 first- and third-semester students enrolled in an Applied Bachelor program in Mechatronics Engineering Technology who had previously used ChatGPT. A 30-item, five-point Likert questionnaire assessed frequency of use, theory, report writing, coding, design, and usage behavior. Descriptive statistics and Cronbach's alpha were calculated. Overall use was moderate (M = 3.270). Theory had the highest mean (M = 3.490), followed by design (M = 3.290), frequency of use (M = 3.255), coding (M = 3.243), reports (M = 3.180), and usage behavior (M = 3.161). The full instrument yielded alpha = 0.923, whereas Reports (alpha = 0.581) and Usage Behavior (alpha = 0.450) showed weak internal consistency. The findings indicate that ChatGPT primarily supported conceptual learning; broader interpretation requires caution.</p>2026-08-05T07:55:20+00:00##submission.copyrightStatement##https://www.motivection.imeirs.org/index.php/motivection/article/view/543Counterweight Reinforcement versus Slope Geometry Redesign for the Block 16 Sidewall at PT XYZ2026-08-07T07:48:17+00:00Bambang Heriyadibambangh@ft.unp.ac.idRinaldo Maretto Patar Simangunsongrinaldomareto8@gmail.comYozsi Mingsi Anepertayosziperta@ft.unp.ac.idHeri Prabowoheri.19782000@ft.unp.ac.idFariz Adityafarizaditya@unp.ac.id<p>Landslides occurred on the Block 16 sidewall at PT XYZ, where the upper benches comprise low-stability fill and the lower benches comprise bedded rock. This study characterized the rock mass, assessed discontinuity-controlled failure potential, calibrated the failed-slope condition by back analysis, and compared counterweight reinforcement with slope-geometry redesign. Scanline mapping, Rock Mass Rating (RMR), stereographic kinematic analysis, Slope Mass Rating (SMR), and Bishop-simplified limit-equilibrium simulations in Slide 6.0 were applied. The lower rock benches had an average RMR of 69.5 and an SMR of 68.6. Oblique-toppling criteria were satisfied by 7.62% of the analyzed discontinuities. The initial model yielded an SF of 1.239, while back analysis reproduced the failure condition at SF = 0.995. Counterweighting increased SF to 1.324 using 42.6 m³/m of fill; geometry redesign produced SF = 1.352 with 264.98 m³/m of excavation. Both alternatives met the SF > 1.3 criterion, but counterweighting required substantially less material handling.</p>2026-08-05T08:09:20+00:00##submission.copyrightStatement##https://www.motivection.imeirs.org/index.php/motivection/article/view/552Quantifying Manual Safeguard Failure Probability in High-Flow Gasoline Tank Overfill: An Integrated SLIM–SHIPP–ETA–LOPA Framework for SIL Specification2026-08-05T09:06:59+00:00Rafi Adisasmito Syamsudinrafi.adisasmito@gmail.comAhmad Daniahmaddanii2887@gmail.comIndrawan ChristanoAnochrist43@gmail.comAdhitya Ryan Ramadhaniadhitya.rr@universitaspertamina.ac.id<p>High-flow gasoline tank filling can escalate to overfill, fire, or explosion when level control relies on manual safeguards. This study quantified manual safeguard failure in a gasoline storage facility operating at 300 kL/h and specified the required automation level. HAZOP identified the critical deviation, ALOHA characterized consequence zones, SLIM estimated operator error probabilities, SHIPP and ETA represented sequential barrier failure, and LOPA determined the required risk reduction. The “More Level” deviation obtained the highest HAZOP priority. Under the adopted modeling assumptions and an assumed initiating event frequency of 1.0 demand per year, ETA estimated failure probabilities of 3.10 × 10⁻¹ for the Ignition Prevention Barrier and 8.54 × 10⁻¹ for the Escalation Prevention Barrier, producing a major fire/explosion scenario frequency of 1.88 × 10⁻³ per year. This value exceeded the adopted target risk of 1.0 × 10⁻⁴ per year and required an RRF of 18.8. The results support implementing a SIL 1 Safety Instrumented System to supplement manual safeguards.</p>2026-08-05T09:06:59+00:00##submission.copyrightStatement##