Garuda - Garba Rujukan Digital

Subardin Subardin

Universitas Halu Oleo

Author-ID : 5364914

Computer Science & IT Control & Systems Engineering

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Articles

PERANCANGAN DAN IMPLEMENTASI SISTEM REPLIKASI DATABASE TERDISTRIBUSI PADA FAKULTAS TEKNIK UNIVERSITAS HALU OLEO Muhammad Hidayat Darmawan; Isnawaty Isnawaty; Subardin Subardin
semanTIK Vol 4, No 2 (2018): semanTIK
Publisher : Informatics Engineering Department of Halu Oleo University

Current technological developments are very influential on the process of data distribution that is demanded fast because every second of information can change. One of the methods used in data exchange is to use data replication in a distributed database. The speed of the process and the number of rows that can be replicated in units of time are some of the things that can be taken into consideration when trying to determine how to replicate data in a distributed database.In replication, a software is used to find or more precisely track changes that occur in a database. After changes in one database are identified and known, then changes are made so that all databases are the same as other databases.This study implements two computers consisting of master and slave, each of which is a database server that is connected to each other in the network. The test results obtained in this study are that with the database replication, every data that is stored periodically always backups the slave computer.Keywords—Replication, Database, Distributed Database DOI : 10.5281/zenodo.1493829

MONITORING SUHU DAN KELEMBABAN PROSES DEKOMPOSISI PUPUK KOMPOS BERBASIS ANDROID Fitri Amaliah; Isnawaty Isnawaty; Subardin Subardin
semanTIK Vol 6, No 1 (2020): semanTIK
Publisher : Informatics Engineering Department of Halu Oleo University

Kelembaban memegang peranan penting dalam proses dekomposisi pupuk sehingga kelembaban ideal harus dijaga pada kisaran 40% hingga 60% sedangkan suhu pupuk pada kisaran 30°C hingga 60°C. Masalah yang sering terjadi dalam pembuatan pupuk kompos adalah tingkat kematangan pupuk yang belum sempurna yang disebabkan oleh tingkat kelembaban dan suhu dalam proses pembuatan tidak stabil. Umumnya pemantauan suhu dan kelembaban pada proses dekomposisi dilakukan secara manual yaitu dengan mengikuti kebiasaan para petani pupuk sehingga waktu dan kinerja tidak efisien. Namun dengan menggunakan komponen mikrokontroler berbasis android pada proses dekomposisi pupuk dapat memberikan kemudahan untuk memperoleh nilai suhu dan kelembaban pupuk kompos secara lebih akurat. Sensor DHT22 digunakan untuk mengukur suhu dan kelembaban sedangkan NodeMcu ESP8266 sebagai mikrokontroler board untuk mengolah data yang didapatkan dari sensor. Dengan implementasi perangkat lunak menggunakan framework ionic untuk smartphone android, dan arduino untuk mikrokontroler. Proses monitoring suhu dan kelembaban pupuk yang menerapkan monitoring realtime dapat berjalan dengan baik melalui internet dengan latency yang sangat kecil.Kata kunci; Monitoring, Dekomposisi, Mikrokontroler, Realtime, Android

Prediksi Popularitas Novel Berbasis Fitur-Fitur Teks Menggunakan Metode Random Forest Nadya Elfareta Azarin; Rizal Adi Saputra; Subardin Subardin
Jurnal Ilmiah Informatika Vol. 9 No. 1 (2024): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v9i1.57-62

In today's digital era, a novel's popularity is often measured by reader response and sales. This research aims to develop a novel popularity prediction model based on text features to provide insights to authors and publishers about the factors that influence reader acceptance. The method used in this research is Random Forest, a machine learning algorithm that can handle classification and regression well. The main goal of this research is to develop a predictive model that can identify key factors that contribute to the popularity of novels. The proposed method integrates text features, such as keyword extraction and sentiment analysis, in a Random Forest framework to predict popularity with high accuracy. The dataset used consists of various novel information, including title, genre, number of pages, and text features such as summary or description. Data is preprocessed to address issues such as missing values and duplicates. Feature extraction is carried out by applying tokenization, stemming, and converting text into TF-IDF vectors. A Random Forest model was built incorporating these features, and the model parameters were optimized through a cross-validation process. The dataset used consists of various novel information, including title, genre, number of pages, and text features such as summary or description. Data is preprocessed to address issues such as missing values and duplicates. Feature extraction is carried out by applying tokenization, stemming, and converting text into TF-IDF vectors. A Random Forest model was built incorporating these features, and the model parameters were optimized through a cross-validation process. The experimental results show that the Random Forest model is able to predict the popularity of novels with a satisfactory level of accuracy. Text features, such as keyword frequency and sentiment analysis, proved significant in their contribution to the predictive ability of the model. These findings provide valuable insight to authors and publishers in understanding reader preferences and the potential success of a novel.

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