Bachelor's Degree in Industrial Engineering
Thesis focused on improving the mechanical availability of Train Loading Station 103 using FMEA and AHP.
Portofolio
Profile, academic journey, digital tools, and engineering projects that turn field activity into operational insight.
Personal Information
Education
Thesis focused on improving the mechanical availability of Train Loading Station 103 using FMEA and AHP.
Final project developed an IoT-based automation and monitoring system for periodic air-conditioner maintenance.
Work Experience
Project
Belt Conveyor Monitoring
Select an area and conveyor to review the belt layout, material-flow animation, and latest inspection results.
AI Workflow Automation
Inspector cukup mengirim foto berlabel melalui Telegram. Sistem membaca, memvalidasi, mencatat, meneruskan, dan memantau penyelesaiannya dalam satu alur tertutup.
Open Telegram BotFoto, tulisan, identitas inspector, dan waktu masuk diterima bot.
PHOTO + CAPTIONAI mengekstrak jalur, area, lokasi detail, jenis temuan, kondisi, dan pengukuran.
STRUCTURED DATAJenis temuan menentukan bidang; aturan menentukan kategori, severity, dan PIC.
RULE-BASED ROUTINGFoto asli diarsipkan, data mentah masuk staging, dan data valid masuk Data_Temuan.
TRACEABLE RECORDPrioritas menentukan tujuan notifikasi, lalu START dan CLOSE memperbarui status beserta foto perbaikan.
CLOSED LOOPTemuan tetap masuk database dan dashboard untuk histori, tren, serta evaluasi rutin.
Bot memilih PIC berdasarkan area, jalur, dan bidang; executor mengirim START lalu foto CLOSE sebagai bukti.
Jumlah temuan, status open/closed, jalur, tanggal, dan tren dibaca langsung dari database.
Setiap pagi sistem mengirim backlog, severity, penyelesaian, temuan kritis, dan grafik ringkas.
Foto awal, routing PIC, perubahan status, catatan, dan foto perbaikan terhubung ke satu ID temuan.





Temuan baru dibandingkan severity kritis.
Contoh data yang siap ditelusuri dan ditindaklanjuti.
| ID Temuan | Jalur | Jenis | Severity | Status |
|---|---|---|---|---|
| INS-2607-124 | CC 11 | Idler | High | Assigned |
| INS-2607-119 | CC 12 | Temperature | Emergency | Open |
| INS-2607-108 | CV 05 | Belt Conveyor | Medium | Closed |
ID_TemuanAreaJalur_ConveyorJenis_TemuanKode_KerusakanSeverityStatusPIC_Nama
MO Digital
Maintenance Order Digital connects planners, field executors, SharePoint records, and SAP status updates in one traceable operational workflow.
The official work order is prepared as the execution basis.
SAP order information is recorded in the shared digital workspace.
The executor performs the job against the issued scope and standard.
Completion evidence and execution notes are submitted through the application.
The planner validates the report and updates the final SAP status.


Dashboard Portfolio
Explore nine interactive Power BI reports covering maintenance, engineering, administration, performance, stock, and inspection. The complete 60-screen archive is included below as portfolio evidence.
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Visual archive
AI Concept Development
A computer-vision concept that continuously analyzes conveyor video, recognizes normal coal flow, and visually identifies foreign or non-coal material before it reaches downstream equipment.
Hard rock and metal carried by the conveyor can damage coal-handling equipment, cause downtime, and increase maintenance costs.
AI analyzes conveyor video in real time, detects foreign material, and immediately warns the operator before equipment damage occurs.
Real-time video analytics, object detection using YOLO/CNN, and streaming with edge-computing capability.
Designed for industrial cameras at key conveyor points and integration with the control-room alarm system.
AI Research Roadmap
An image-analysis concept for classifying coal types, estimating particle-size distribution, and predicting calorific value from submitted images using features calibrated against verified laboratory data.
Calorific value is an AI prediction and must be validated with representative sampling and laboratory analysis before operational use.
Laboratory calorific-value testing takes time, so field quality decisions can be delayed while teams wait for verified results.
A deep-learning model estimates calorific value from coal photographs as an early decision aid before laboratory validation.
Deep learning with CNN, image regression, and a calibrated dataset connecting coal photographs with verified calorific values.
A simple photo-based application intended for direct field use by the coal quality-control team.
AI Safety Monitoring
A computer-vision and thermal-monitoring concept for detecting flame, smoke, and abnormal heat signatures around conveyor transfer points before they become operational incidents.
Fire detection is an early-warning AI concept and must be validated with site cameras, lighting conditions, and emergency-response procedures before operational use.
Continuous Improvement & Engineering
Expanded vibration and thermography monitoring to improve drive-unit reliability and enable earlier failure prevention.
Used FMEA and AHP to identify repair-process inefficiencies, improve pre-maintenance inspection, and reduce excessive downtime.
Applied fishbone analysis and real-time vibration and temperature monitoring to detect abnormal trends before equipment damage.
Created a shared inspection database that reduced duplication, accelerated updates, and improved communication with planners.
Replaced manual reporting with a collaborative real-time system, improving monthly reporting efficiency and progress visibility.
Developed a web-based monitoring and alert system to capture eligible warranty claims and optimize spare-part procurement costs.