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All Journal Jurnal Teknik Informatika dan Teknologi Informasi Journal of Management and Informatics
Candra Supriadi
Universitas Sains dan Teknologi Komputer
Computer Science & IT Decision Sciences, Operations Research & Management

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SISTEM INFORMASI PENGHITUNGAN HASIL PRODUK BERBASIS INTERNET OF THINGS Muhammad Abdul Khalim; Andreas Heri Kurniawan; Candra Supriadi
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 2 No. 1 (2022): April: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Politeknik Pratama Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (300.136 KB) | DOI: 10.55606/jutiti.v2i1.369

Abstract

Perkembangan teknologi saat ini sangat pesat, hal ini dapat dibuktikan dengan banyaknya alat-alat yang diciptakan manusia untuk mempermudah dalam kehidupan. Didalam industri berbagai macam pekerjaan dilakukan dengan cepat sehingga dengan counter yang bersifat manual akan menghambat seperti barang-barang.Pada PT. APPAREL ONE INDONESIA untuk menghitung sebuah outputan harus di lakukan dengan manual atau dengan menekan satu persatu, maka dari itu peneliti memecahkan solusi dengan membuat sistem penghitungan barang otomatis berbasis Internet of Things (IoT). Sistem utama dirancang menggunakan Sensor ultrasonik,sensor berat dan Nodemcu esp8266. Dengan metode prototype untuk mensimulasikan sistem ini sebelum di terapkan di lapangan produksi untuk mengetahui cara kerja sistem. Berdasarkan pengujian dengan cara object didekatkan dengan sensor ultrasonik dan berat untuk mengetahui jumlah barang secara otomatis yang dihasilkan setiap harinya, tanpa harus melakukan penghitungan secara manual. Selain penghitungan otomatis, sistem ini juga melakukan penginputan secara otomatis dan data tersebut tersimpan di database
Enhancing Performance Using New Hybrid Intrusion Detection System Candra Supriadi; Charli Sitinjak; Fujiama Diapoldo Silalahi; Nia Dharma Pertiwi; Sigit Umar Anggono
Journal of Management and Informatics Vol 1 No 2 (2022): Agustus: Journal of management and informatics
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v1i1.134

Abstract

Intrusion Detection Systems (IDS) are an efficient defense against network attacks as well as host attacks as they allow network/host administrators to detect any policy violations. However, traditional IDS are vulnerable and unreliable for new malicious and genuine attacks. In other case, it is also inefficient to analyze large amount of data such as possibility logs. Furthermore, for typical OS, there are a lot of false positives and false negatives. There are some techniques to increase the quality and result of IDS where data mining is one of technique that is important to mining the information that useful from a large amount of data which noisy and random. The purpose of this study is to combine three technique of data mining to reduce overhead and to improve efficiency in intrusion detection system (IDS). The combination of clustering (Hierarchical) and two categories (C5, CHAID) is proposed in this study. The designed IDS is evaluated against the KDD'99 standard Data set (Knowledge Discovery and Data Mining), which is used to evaluate the efficacy of intrusion detection systems. The suggested system can detect intrusions and categorize them into four categories: probe, DoS, U2R (User to Root), and R2L (Remote to Local). The good performance of IDS in case of accuracy and efficiency was the result of this study.
Co-Authors Fujiama Diapoldo Silalahi Kurniawan, Andreas Heri Muhammad Abdul Khalim Nia Dharma Pertiwi Sigit Umar Anggono Sitinjak, Charli