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All Journal Jurnal Informatika Universitas Pamulang Jurnal Sistem Komputer & Kecerdasan Buatan Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen
Agung Budi Susanto
Universitas Pamulang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Analisis Enterprise Resource Planning dalam Persediaan Material dengan Metode Manufacturing Resource Planning Menggunakan Software Odoo 13 Manufacturing (Studi Kasus PT. Yuasa Battery Indonesia) Kuntoro, Kuntoro; Susanto, Agung Budi; Kurnia, Dadang
Jurnal Informatika Universitas Pamulang Vol 6, No 4 (2021): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v6i4.13202

Abstract

PT. Yuasa Battery Indonesia is a manufacturing company engaged in the manufacture of batteries for motor vehicles, both cars and motorcycles. The production system in this company is make to order. The production process that occurs is that the recording of production activities is still carried out in their respective work sections such as manufacturing, inventory, purchasing so that the information needed for the production line is slow and inaccurate which results in the production plan not being achieved. The purpose of this research is to integrate data and information in all lines of the company so that the constraints faced by the company can be overcome. The material flow for the production process is controlled using the Material Requirement Planning method while the software used to integrate data and information in the production line is Odoo 13 manufacturing. The results showed an increase in productivity from the planned production of 128,387 pcs, 128,381 pcs with and 6 pcs was achieved and the percentage of production achievement was 100% and users accepted the implementation of Odoo 13 with an average test result of 82.3%.
Analisis Sentimen Vaksinasi Covid-19 Pada Twitter Menggunakan Naive Bayes Classifier Dengan Feature Selection Chi-Squared Statistic dan Particle Swarm Optimization Ristasari Dwi Septiana; Agung Budi Susanto; Tukiyat Tukiyat
Jurnal SISKOM-KB (Sistem Komputer dan Kecerdasan Buatan) Vol. 5 No. 1 (2021): Volume V - Nomor 1 - September 2021
Publisher : Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47970/siskom-kb.v5i1.228

Abstract

Tingginya penyebaran Covid-19 semakin berdampak pada bidang kesehatan, ekonomi, bahkan bidang pendidikan di Indonesia, sehingga pemerintah Indonesia melakukan tindakan vaksinasi Covid-19 guna menekan tingkat penyebaran Covid-19 di Indonesia. Namun hal tersebut dinilai kotroversial sehingga menarik perhatian masyarakat untuk memberikan opini di berbagai media seperti media sosial twitter. Sehingga membutuhkan analisa sentimen masyarakat terhadap upaya pemerintah pada tindakan vaksinasi Covid-19 untuk mencapai hasil prediksi dengan nilai akurasi paling optimal. Proses crawling secara otomatis menggunakan tools Rapidminer akan mengambil data tweets yang mengandung 5 (lima) kata kunci, yaitu “Vaksin Sinovac”, “Vaksin Astrazeneca”, “Vaksin Moderna”, “Vaksin Merah Putih”, dan “Vaksinasi Covid-19”. Dataset tweets didapatkan dari tanggal 4 Agustus 2021 sampai 12 Agustus 2021. Dataset diperoleh sejumlah 2060 tweets dan diberi label secara manual didapatkan jumlah tweet sebanyak 1193 sentimen positif, 73 negatif, dan 794 netral. Data tersebut dianalisa dengan menggunakan Metode Feature Selection Chi-Squared Statistic dan Particle Swarm Optimization (PSO) untuk mengurangi atribut yang kurang relevan pada saat proses klasifikasi dengan algoritma Naive Bayes Classifier (NBC). Hasil pengujian menunjukan bahwa Algoritma Naive Bayes Classifier (NBC) tanpa Feature Selection mendapatkan nilai akurasi 63,69%. Hasil penelitian menunjukkan bahwa Algoritma Naive Bayes Classifier (NBC) dengan Feature Selection Chi-Squared Statistic mempunyai tingkat akurasi 69,13%. Sedangkan hasil pengujian algoritma Naive Bayes Classifier (NBC) dengan Particle Swarm Optimization mempunyai tingkat akurasi 66,02%. Dengan demikian hasil seleksi fitur Chi-Squared Statistic mendapatkan nilai akurasi yang lebih baik jika dibandingkan dengan Particle Swarm Optimization untuk proses klasifikasi algoritma Naive Bayes Classifier (NBC) dengan selisih akurasi 3,11%.
Analisis Pengelolaan Konferensi Nasional Pendekatan Business Process Management Menggunakan Value-Added Analysis dan Root-Cause Analysis Zafira Salsabilah; Agung Budi Susanto; Taswanda Taryo
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 13 No 2 (2023): September 2023
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v13i2.494

Abstract

Conference management is one of the core business processes at the Research and Community Service Institute (LPPM) and it is conducted by online. The problem is the lack of a business process model for conference management, hence the committee had not a clear understanding of its activities. Furthermore, there has been no evaluation of the business processes, leading to unidentified weaknesses in the activities. The purpose of this research is to map and summarize the business processes using Business Process Management (BPM). BPM proves to be suitable for achieving the research objectives as it focuses on process approaches and provides tools and techniques to enhance the quality of conference management. The analysis employs qualitative process analysis with Value-Added Analysis and Root-Cause Analysis using the Why-Why Diagram approach to identify the root causes of issues and propose improvement measures for each business process. The research methodology used is qualitative with data collection technique involving interview, observation, and literature studies. The business process modeling adopts the Business Process Modelling Notation (BPMN). The research outcome presented a to-be process model that illustrated the recommended conference management business process which consisting of 7 main business processes, 27 sub-processes, 21 activities, and 200 tasks.
Co-Authors Dadang Kurnia, Dadang Kuntoro Kuntoro Ristasari Dwi Septiana Salsabilah, Zafira Taswanda Taryo Tukiyat Tukiyat