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All Journal Jurnal Buana Informatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Format : Jurnal Imiah Teknik Informatika ComTech: Computer, Mathematics and Engineering Applications bit-Tech eCo-Buss Algor SATIN - Sains dan Teknologi Informasi
Junaedi
Universitas Buddhi Dharma
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Mathematics

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Journal : bit-Tech

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Smart Aquarium with IoT based as Monitoring in Fish Farming Junaedi Junaedi; Hok Ki
bit-Tech Vol. 4 No. 3 (2022): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v4i3.441

Abstract

The development of science and technology has developed rapidly at this time and will have a positive impact to facilitate human activities, including aquarium ornamental fish hobbyists. The problems encountered are, ornamental fish sellers who have difficulty monitoring all aquarium conditions and feeding and for people who are very busy and even indifferent to monitoring and feeding their ornamental fish. This tool has a water heater as well as a water temperature and pH sensor that can directly monitor the conditions in the aquarium. Arduino Uno is the mainboard that is used to control all sensors with Internet of Things (IoT) including : water temperature sensors, water turbidity sensors, water level sensors, water pH sensors, automatic lights, water heaters that will automatically turn on when the temperature is low and will automatically turn off when they reach the specified temperature and ESP8266-01 which functions to communicate between the Board and the smartphone, as well as the components of the tool and its uses are in accordance with requests from users obtained through Requirement Elicitation. Through the BlackBox trial, it includes low temperature testing of the water which makes the water heater automatically turn on and the water heater turns off when it reaches the specified temperature, monitoring water pH, water temperature, water level, water turbidity, on & off lights and feeding fish manually via a smartphone. And for the results of the Smart Aquarium trial, it can be operated with an Android smartphone, at least Android OS 4.3 and above.
Implementation of Linear Regression Algorithm to Predict Stock Prices Based on Historical Data Jelvin Putra Halawa; Aditiya Hermawan; Junaedi .
bit-Tech Vol. 5 No. 2 (2022): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v5i2.616

Abstract

Stock investment is in great demand by investors because it can provide large profits with large risks or losses, in accordance with the investment principle of low risk low return, high risk high return. Stock prices that fluctuate in a very short time make it difficult for investors to predict stock prices in the future, so investors must pay more attention and gather as much information as possible regarding the shares to be bought or sold. This study aims to create a data mining model using a Linear Regression algorithm that can predict daily stock closing prices to provide information that supports investors in stock transactions. The data used is historical data on daily stock prices for 10 companies in the last 8 years for the period 25 February 2013 – 25 February 2021. Historical stock price data will be prepared using the Noving Average method and create a data mining model using the linear regression method to generate stock price prediction models. The resulting model can be used to predict stock prices well enough to assist investors in making investment decisions to obtain large profits with low risk.
User Interface Experience Analysis of PMB Online Buddhi Dharma Using System Usability Scale Junaedi; Ardiane Rossi Kurniawan Maranto; Maysha Permata Putri; Suwitno
bit-Tech Vol. 6 No. 2 (2023): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v6i2.1051

Abstract

In the era of advanced digital technologies, the admission process for new students (PMBs) has become a critical aspect of education. To streamline and expedite this process, educational institutions are increasingly utilizing online enrollment applications. One such application, PMB Online Buddhi Dharma, plays a crucial role in this context. However, the success of these applications is not solely determined by technical ease; user experience, particularly the User Interface (UI), plays a pivotal role in influencing user satisfaction and efficiency. This study employs the System Usability Scale (SUS) method to comprehensively analyze the UI of the PMB Online Buddhi Dharma application, providing insights into usability and user satisfaction. Drawing from previous studies utilizing SUS in similar contexts, the research aims to contribute to the development and enhancement of the application's UI. This research evaluates too the effectiveness of Buddhi Dharma University's PMB Online in meeting the digital registration needs of prospective students, emphasizing ease of use and user acceptance. Through the SUS method, the study assesses user satisfaction and ease of use, obtaining an average SUS score of 78 from 30 respondents. This score categorizes Buddhi Dharma University Online PMB as "good," indicating a commendable level of acceptance from users, predominantly prospective students. The research concludes with implications for the application's further improvement and development, emphasizing the importance of user-friendly interfaces in digital admission processes.
Analysis and Design of Breeding Management Information System in Poultry Farms Alexius Hendra Gunawan; Verry Kuswanto; Junaedi
bit-Tech Vol. 6 No. 3 (2024): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v6i3.997

Abstract

Breeding management is indispensable in business development in the field of poultry farming where companies are required to keep up with the development of the digitalization era. Companies must be able to take advantage of opportunities in advances in information technology in obtaining accurate, fast and reliable information so that management can make decisions. PT. Peternakan Ayam Manggis has been using manual methods in recording the results of production or the process of breeding chicken farms. in making records using office software, namely excel, so that errors often occur and are often not realtime in making records. Records generated by poultry production include depletion, quarantine, culling, feed and egg production. in processing data generated manually can make it difficult for management to make decisions. The purpose of this research is to design an application that can be used by companies to obtain information during the breeding process.The research method in developing this application uses waterfall and design role analysis with data collection and using UML modeling. This information system application produces information about Breeding management including depletion, quarantine, feed, egg production, movement of chickens from one cage to another, culling. this information system application uses a database and application blackbox testing is carried out so that the application runs as needed.  
Eksplorasi Algoritma Support Vector Machine untuk Analisis Sentimen Destinasi Wisata di Indonesia Junaedi; Alexius Hendra Gunawan; Verri Kuswanto; Jonathan
bit-Tech Vol. 7 No. 2 (2024): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i2.1810

Abstract

Penelitian ini mengeksplorasi penerapan algoritma Support Vector Machine (SVM) dalam Text Mining untuk analisis sentimen sektor pariwisata di Indonesia, menggunakan data dari platform Twitter. Data dikumpulkan melalui API Twitter dan diproses melalui tahapan prapemrosesan teks, termasuk tokenisasi, normalisasi, penghapusan stopword, dan stemming, untuk memastikan kesiapan data dalam analisis. Model SVM diuji dengan tiga kernel berbeda—linear, radial basis function (RBF), dan sigmoid—serta menggunakan rasio data latih-uji 7:3 dan 8:2. Hasil menunjukkan bahwa kernel linear dengan rasio 7:3 menghasilkan kinerja terbaik dengan akurasi 92,89%, precision 92%, recall 74%, dan F1-score 81%. Evaluasi berdasarkan kelas sentimen menunjukkan performa tinggi pada sentimen positif (F1-score 96%) tetapi moderat pada kelas netral (F1-score 67%), mencerminkan pengaruh ketidakseimbangan data. Penelitian ini memberikan kontribusi signifikan dalam mendukung pengambilan keputusan berbasis data untuk pengembangan sektor pariwisata. Temuan ini memungkinkan pengelola destinasi wisata untuk memahami opini wisatawan secara otomatis, menyusun strategi promosi yang lebih efektif, serta meningkatkan kualitas layanan. Dengan menerapkan analisis sentimen berbasis SVM, penelitian ini mendukung pengelolaan pariwisata berbasis data untuk meningkatkan daya saing destinasi wisata di Indonesia. Penelitian lanjutan disarankan untuk mengatasi ketidakseimbangan data melalui teknik resampling atau penerapan algoritma alternatif seperti deep learning, guna meningkatkan akurasi klasifikasi sentimen yang lebih kompleks. Dengan demikian, penelitian ini menjadi langkah strategis dalam memanfaatkan teknologi analitik untuk pengelolaan pariwisata yang lebih inovatif.
Enhancing Stock Price Forecasting: Optimizing Neural Networks with Moving Average Data Aditiya Hermawan; Stanley Ananda; Junaedi; Edy
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2196

Abstract

This research focuses on optimizing a neural network model for stock price prediction using Particle Swarm Optimization (PSO), considering the inherent risks and potential high returns associated with stock investment. Given the challenges posed by stock price volatility, this study combines Moving Average (MA) a fundamental statistical technique in stock market analysis with advanced data mining approaches, specifically neural networks and PSO, to enhance prediction accuracy. The primary objective is to improve the efficiency of neural networks by minimizing error rates and equipping investors with more reliable tools for financial decision-making. The proposed methodology involves converting historical stock price data into a Simple Moving Average (SMA) over a 5-day period, followed by optimizing a neural network model using PSO. This optimization process fine-tunes key parameters, particularly the weight distributions of various stock market indicators, including Open SMA, High SMA, Low SMA, and Close SMA. Model performance is evaluated using Root Mean Square Error (RMSE) as a validation metric. The findings indicate a significant enhancement in the predictive accuracy of the neural network model after PSO optimization. The optimal configuration is identified in a two-layer neural network with a specific node arrangement. This optimized model not only improves stock price forecasting precision but also has practical implications for investors and financial analysts in risk management and profit maximization.
Co-Authors Abidin Abidin Aditiya Hermawan Alexius Hendra Gunawan Alexius Hendra Gunawan Ardiane Rossi Kurniawan Maranto Benny Daniawan Deviastati Putri Sugiarta Karlim Edy Hok Ki Jelvin Putra Halawa Jonathan Leonardo Lianata Luis Alpianto Mariana Purnamasari Maysha Permata Putri Oscar Hasan Putra Riki Riki Stanley Ananda Suwitno Verri Kuswanto Verry Kuswanto Yusuf Kurnia