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All Journal Jurnal Ilmiah FIFO JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Manajemen Informatika Jayakarta
Hendarman Lubis
Universitas Bhayangkara Jakarta Raya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology

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Perancangan Model Aplikasi Monitoring Kesehatan dan Aktifitas Kerja Harian Berbasis KPI Pada Masa Pandemi COVID-19 Andy Achmad Hendharsetiawan; Hendarman Lubis; Kusdarnowo Hartanto
Jurnal Ilmiah FIFO Vol 13, No 1 (2021)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2021.v13i1.003

Abstract

Pada masa pandemi COVID-19 yang belum berakhir ini bahkan beberapa negara termasuk Indonesia menunjukkan adanya peningkatan data penderita yang cukup tinggi maka pemerintah terus memberlakukan pembatasan sosial termasuk mengurangi jam kerja bagi perusahaan dan industri termasuk pada pegawai pemerintahan.  Dalam kondisi pandemi COVID-19 ini dan upaya untuk tetap menjaga produktifitas kerja yang baik maka diperlukan suatu system aplikasi untuk menunjang kebutuhan tersebut. Perancangan model sistem aplikasi ini diharapkan dapat membantu perusahaan untuk memantau kondisi kesehatan dan aktifitas harian setiap karyawan di perusahaan atau instansi tersebut. Guna menjaga produktifitas kerjanya maka salah satunya harus dimonitor aktifitas kerja harian karyawan khususnya pada masa Work From Home (WFH) dengan baik dan mudah serta berbasis Key Performance Indicator (KPI) untuk memudahkan memantau dan mengevaluasi kinerja setiap karyawan.  Tulisan ini akan menjelaskan mengenai gagasan perencanaan model suatu sistem aplikasi yang digunakan setiap karyawan suatu perusahaan untuk memantau kesehatan dan aktifitas kerja harian karyawannya khususnya pada situasi WFH di masa pandemi COVID-19.
Implementasi Sistem Data Mining Untuk Menentukan Stock Accuracy Pada Warehouse PT Coca-Cola Amatil Indonesia Cibitung–Plant Hendarman Lubis; Dwi Budi Srisulistiowati
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 4, No 1 (2020): Januari 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v4i1.1795

Abstract

Abstract−Data Mining is a process of extracting data or filtering data that utilizes large data sets through a series of processes to obtain information that stands out from the data. PT. Coca-Cola Amatil Indonesia Cibitung-Plant has one of the largest warehouse in Indonesia exactly warehouse mega distribution center (DC). With ±32000 m2 warehouse area or equivalent to ±30000 Pallets. To maintain the accuracy of the stock in the warehouse of course required a good system in order to support the operational activities in the warehouse, one way to maintain the accuracy of stock in the warehouse is to do the overall product calculation in the warehouse (Stock Opname), in order to know the accuracy data in the system with the physical stock in the warehouse. With the data transactions stored in the database, sometimes the transaction data is only on leave to accumulate without any further action, then make the information system that manages the data to dig information by data mining techniques.
EVALUASI SENTIMEN MASYARAKAT TERHADAP KEBIJAKAN SUBSIDI KENDARAAN LISTRIK DI INDONESIA DENGAN PENDEKATAN INSET LEXICON, WORD EMBEDDING, DAN ALGORITMA SUPPORT VECTOR MACHINE Ridwan Ridwan; Hendarman Lubis
Jurnal Manajamen Informatika Jayakarta Vol 6 No 2 (2026): JMI Jayakarta (April 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jmijayakarta.v6i2.2364

Abstract

The electric vehicle subsidy policy in Indonesia is one of the government's efforts to promote environmentally friendly energy usage and reduce carbon emissions. However, the implementation of this policy has generated diverse public responses, which can be analyzed through social media platforms. This study aims to evaluate public sentiment toward the electric vehicle subsidy policy in Indonesia using the InSet Lexicon approach, Word Embedding (Word2Vec), and the Support Vector Machine (SVM) algorithm. The dataset was collected from Twitter through a crawling process based on relevant keywords, resulting in 1,000 tweets. The research stages include text preprocessing, sentiment labeling using InSet Lexicon, feature extraction using Word2Vec, and classification using SVM. The results show that sentiment distribution consists of 45% positive, 35% negative, and 20% neutral. The classification model achieved an accuracy of 86%, precision of 83%, recall of 81%, and an F1-score of 82%. These results indicate that the proposed approach is effective in classifying sentiment. Furthermore, the use of Word Embedding improves text representation quality, which contributes to better model performance. This study provides insights into public perception and can serve as a reference for evaluating public policies.
Co-Authors Andy Achmad Hendharsetiawan Dwi Budi Srisulistiowati Hantoro, Kusdarnowo Ridwan Ridwan