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All Journal CESS (Journal of Computer Engineering, System and Science) Jurnal Teknologi Elektro Sistemasi: Jurnal Sistem Informasi Jurnal Teknik Komputer AMIK BSI JITK (Jurnal Ilmu Pengetahuan dan Komputer) J I M P - Jurnal Informatika Merdeka Pasuruan Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Jurnal Mantik INTI Nusa Mandiri Jurnal Sistem Informasi Journal of Information System and Technology (JOINT) Jurnal Mandiri IT Teknologika Innovative: Journal Of Social Science Research
Indra Chaidir, Indra
Universitas Bina Sarana Informatika
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Public Health Other

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Mysql Database Syncronization Using Restful Webservice Api PT. Minori Felix Wuryo Handono; Sumarna; Hafis Nurdin; Fernando B Siahaan; Hary Sugiarto; Indra Chaidir
Jurnal Mantik Vol. 5 No. 2 (2021): Augustus: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol5.2021.1409.pp855-859

Abstract

PT. Minori is an apprentice delivery company to Japan located in Cikarang Bekasi. The apprentices to be sent to Japan must first follow the selection and education process. Even after arriving in Japan, the apprentices must take part in lessons too so they will not forget the educational material and move up to a higher level. The learning method during education is by accessing the Education server in LAN (Local Area Network) and accessing the cloud server during internship. The difference in location and network causes problems, the data that is on the local network server with the public network server, how to synchronize the data without changing the existing network infrastructure. Database synchronization in PT Minori is done by restful API from both servers to synchronize data.
ALAT PENGENDALI LISTRIK JARAK JAUH DENGAN SMS GATEWAY BERBASIS ANDROID Toni Wirawan; Indra Chaidir; Mohammad Badrul
Jurnal Sistem Informasi Vol 3 No 2 (2014): JSI Periode Agustus 2014
Publisher : LPPM STMIK ANTAR BANGSA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (446.368 KB) | DOI: 10.51998/jsi.v3i2.59

Abstract

Abstract— Microcontroller is a microcomputer and microprocessor technology breakthrough, which has been widely used in electronic circuit applications. By utilizing the functionality of a microcontroller and android application, then the application will be made an electric appliance remote control with android. The purpose of this application to meet the needs of people who have mobility is high enough to offset the price of electricity is high enough. This application uses the android application to allow users to remotely control electricity. Electrical home will be on / off as the user desires. Application of electric remote control makes it easy to control remote history of electricity, especially for people who need mobility is high enough and wants everything very practical. Intisari— Mikrokontroler merupakan suatu terobosan teknologi mikrokomputer dan mikroprosesor yang saat ini telah banyak digunakan pada aplikasi rangkaian elektronika. Dengan memanfaatkan fungsi dari mikrokontroler dan aplikasi android, maka akan dibuat sebuah aplikasi alat pengendalian listrik jarak jauh dengan android. Tujuan dari aplikasi ini untuk memenuhi kebutuhan masyarakat yang mempunyai mobilitas cukup tinggi dengan mengimbangi harga dasar listrik yang cukup tinggi Aplikasi ini menggunakan aplikasi android untuk memudahkan pengguna mengendalikan listrik secara jarak jauh. Listrik dirumah akan on/off sesuai keinginan pengguna. Aplikasi pengendali listrik jarak jauh ini memberikan kemudahan mengontrol listrik secara jarah jauh, terutama bagi masyarakat yang kebutuhan mobilitas cukup tinggi dan menginginkan segalanya serba praktis. Kata kunci : Mikrokontroler, Listrik, SMS, Android
PREDIKSI HARGA SAHAM TWITTER DENGAN LONG SHORT-TERM MEMORY RECURRENT NEURAL NETWORK Ibnu Akil; Indra Chaidir
INTI Nusa Mandiri Vol 17 No 1 (2022): INTI Periode Agustus 2022
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v17i1.3277

Abstract

Abstract— Today the trading business has become a trend to get money easily without having to work hard as long as you have capital. To get maximum results and avoid losses, it is necessary to have expertise in predicting the ups and downs of the stock market value. The purpose of this research is to utilize machine learning technology to predict the fluctuation of stock value by using the Long Short-Term Memory RNN model. From the results of this study, it was found that LSTM+RNN is suitable for use in single-step models. Keywords: stock price, machine learning, recurrent neural network, lstm Abstrak—Dewasa ini bisnis trading menjadi suatu trend untuk mendapatkan uang dengan mudah tanpa harus bekerja keras asalkan memiliki modal. Untuk mendapatkah hasil yang maksimal dan menghindari kerugian maka diperlukan keahlian di dalam memprediksi naik turunya nilai bursa saham. Tujuan dari penelitian ini adalah memanfaatkan teknologi machine learning untuk memprediksi naik turunya nilai saham dengan menggunakan model Long Short-Term Memory RNN. Dari hasil penelitian ini didapatkan bahwa LSTM+RNN cocok untuk digunakan pada model single-step. Kata kunci: harga saham, machine learning, recurrent neural network, lstm
Market Basket Analysis untuk Mengetahui Pola Beli Konsumen Roofbox Mobil menggunakan Algoritme Apriori Karlena Indriani; Ade Christian; Hariyanto; Ali haidir; Adi supriyatna; Indra Chaidir
Jurnal Teknologika Vol 12 No 2 (2022): Jurnal Teknologika
Publisher : Sekolah Tinggi Teknologi Wastukancana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51132/teknologika.v12i2.248

Abstract

Roofbox merupakan bagasi tambahan pada atap mobil yang digunakan sebagai ruang penyimpanan tambahan. Antusiasme pasar dalam menyerap permintaan roofbox tercatat semakin tinggi menjelang musim liburan. Roofbox dicari agar penempatan barang bisa menjadi lebih baik. Untuk mengetahui pengelompokkan tipe Roof Box Whale Carrier yang banyak dibeli oleh konsumen membutuhkan teknik dan cara tertentu yang berhubungan dengan transaksi penjualan Roof Box Whale Carrier. Pada penelitian ini menggunakan teknik analisa asosiasi, Teknik asosiasi adalah teknik Data Mining untuk menemukkan aturan asosiatif antara suatu kombinasi. Penting tidaknya suatu aturan asosiatif dapat diketahui dengan dua parameter, yaitu Support (Nilai Penunjang) dan Confidence (Nilai Kepastian). Penjualan Roof Box Whale Carrier yang paling banyak dijual di PT. Sole Indotrade berdasarkan pengolahan data didapat kesimpulan bahwa Algoritme Apriori dengan melihat tipe yang memenuhi Min Support dan Min Confidence. Dengan algoritme apriori dapat ditemukan produk yang paling banyak terjual Roadway dengan support 66,67%, Overlander dengan support 66,67%, Beachroad dengan support 66,67%, FreewayX dengan support 58,33% dan Speed dengan support 41,67. Dengan diketahuinya produk roofbox yang paling sering dibeli, PT SOLE INDOTRADE dapat mengatur stok barang agar tidak terjadinya penumpukan barang yang mengakibatkan kerugian. Untuk tipe roofbox yang paling banyak terjual pada PT. Sole Indotrade adalah Roadway, Overlander dan Beachroad
Collaboration of Nazief & Adriani Stemming Algorithm with PostgreSQL Queries Parsing Method to Search for New Study Program Names Indra Chaidir
CESS (Journal of Computer Engineering, System and Science) Vol 8, No 2 (2023): July 2023
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v8i2.48212

Abstract

Penolakan usulan nama baru program studi vokasi pada Aplikasi Silemkerma di Direktorat Jenderal Pendidikan Tinggi Vokasi, Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi sering terjadi karena terdapat kemiripan nama program studi yang diusulkan dengan nama program studi yang sudah ada di dalam basis data. Banyak data tidak ditemukan karena filter data menggunakan metode konvensional dalam kasus ini menggunakan operator ILIKE dengan pola wildcard character % (percent), sedangkan data yang dicari tersedia di dalam basis data. Ini terjadi dikarenakan operator ILIKE tidak dapat membaca perubahan kata dari leksem/akar kata (root word) seperti "pengelolaan" dengan memiliki prefix dan suffix, dengan akar kata "kelola". Mengatasi permasalahan ini, penulis memanfaatkan Algoritma Nazief & Adriani untuk stemming agar mendapatkan leksem dari kalimat yang dimasukan. Hasil algoritma tersebut terus diolah menggunakan Metode Parsing Queries, salah satu metode Full Text Search yang ada pada basis data PostgresQL. Hasil penelitian ini dapat diimplementasikan pada Aplikasi tersebut.Rejection of new vocational study program name proposals in Silemkerma Application at the Directorate General of Vocational Higher Education, Ministry of Education, Culture, Research, and Technology often occurs because there is a similarity between the proposed study program name and the existing study program name in the database. Many data are not found because the data filter uses conventional methods in this case using the ILIKE operator with the wildcard character pattern % (percent), while the data sought is available in the database. This is because the ILIKE operator cannot read word changes from lexemes/root words such as "pengelolaan" which has a prefix and suffix, with the root word "kelola". Overcoming this problem, the author utilizes the Nazief & Adriani Algorithm for stemming in order to get lexemes from the sentences entered. The results of the algorithm are then processed using the Parsing Queries Method, one of the Full Text Search methods available in the PostgresQL database. The results of this research can be implemented in the application.
Classification of Heart Disease Diagnoses Using Gaussian Naïve Bayes Akil, Ibnu; Chaidir, Indra
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 2 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i2.10114

Abstract

Machine learning, which is part of artificial intelligence, has been widely applied in various fields, especially the medical field. Machine learning helps doctors make more accurate diagnoses. Heart disease is one of the highest causes of death in the world, so the need for accurate diagnosis is absolute for this disease. There are many algorithms that have been applied in machine learning to classify and detect heart disease, such as Linear Discriminant Analysis [1], KNN, Decision Tree, Random Forest [2], and Logistic Regression [3]. One classification algorithm that has not been implemented is Gaussian Naive Bayes. So, in this research the Gaussian Naive Bayes algorithm will be tested on the cardio health risk assessment dataset. From the research results of applying the Gaussian Naive Bayes algorithm to cardio health risk assessment data, accuracy was 0.87%, precision was 0.88%, recall was 0.90%, and f1-score was 0.89%.
Reinforcement learning for bitcoin trading: A comparative study of PPO and DQN Prasetyo, Romadhan Edy; Sumanto, Sumanto; Chaidir, Indra; Supriyatna, Adi
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.455

Abstract

Bitcoin’s high volatility demands automated strategies that adapt to changing market regimes while managing risk. This study compares Proximal Policy Optimization (PPO) and Deep Q-Network (DQN) for Bitcoin trading using hourly BTC/USDT data from 2019 to early 2025. The models are trained to generate buy and sell signals from technical indicators including the Relative Strength Index (RSI), MA20, volatility, Moving Average Convergence Divergence (MACD), volume trend, SMA200, and a weekly trend filter. All features are computed on hourly bars. The evaluation shows that PPO tends to trade more aggressively and delivers higher performance during bullish phases, though with greater risk in unstable markets. By contrast, DQN trades more selectively and maintains better stability in sideways or choppy conditions. These findings support the effectiveness of reinforcement learning for adaptive cryptocurrency trading and highlight complementary strengths between PPO and DQN across market regimes.
Prediksi Harga Emas di Indonesia menggunakan Metode Linear Regression Berbasis Data Historis Antam Cahya, Titus Dwi; Sumanto, Sumanto; Chaidir, Indra
Innovative: Journal Of Social Science Research Vol. 5 No. 4 (2025): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v5i4.21047

Abstract

This study uses a simple linear regression method to predict gold prices in Indonesia using historical Antam gold data. Linear regression is applied to model the linear relationship between the 2024 daily gold price (variable Y) and the date (variable X). Model performance is evaluated using Mean Squared Error (MSE) and R-squared (R²) to ensure more stable and accurate results. The evaluation results show that the linear regression model used has an MSE of 1403425123.8609 and an R² of 0.93, indicating good performance in predicting gold prices. This study concludes that the simple linear regression method can be used to predict gold prices throughout the year (long-term), but cannot accurately predict daily prices.
Application of the Deep Neural Networks Model in Analyzing ChatGPT Application Sentiment Fauzi, Ahmad; Chaidir, Indra; Iqbal, Muhammad; Ginabila, Ginabila
Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i1.3432

Abstract

AI has been able to intelligently mimic human behavior and has been applied in various contexts, including healthcare for more efficient patient care. One of the prominent trends in AI is advanced language models such as ChatGPT developed by OpenAI. The effectiveness of ChatGPT in finding and fixing bugs in computer code is a subject of debate, depending on the task, training data, and system design. The popularity of social media platforms, particularly Twitter, as a data source for text analysis has increased interest in sentiment analysis. This study explores sentiment towards the ChatGPT application using a dataset of 50,000 tweets. Sentiment analysis is performed using a deep neural network (DNNs) approach to achieve optimal accuracy. Deep learning models are known to have high predictivity and efficient training time. Through this experiment, we aim to gain insight into how the public views ChatGPT in three sentiment categories: positive, negative, and neutral. DNN (Deep Neural Network) is proposed because of its good performance and can shorten the amount of training time. The results with the model used in this study, namely CNN and LSTM both achieve an accuracy value of more than 90%: Where CNN obtains an accuracy value of 91.12% and LSTM obtains an accuracy of 90.84%.
Analisis Kepuasan Karyawan Terhadap Pelayanan Helpdesk Menggunakan Metode Customer Satisfaction Indeks pada Kemlu RI Ningrum, Rina Kusuma; Indra Chaidir
Journal of Information System and Technology (JOINT) Vol. 6 No. 3 (2025): Journal of Information System and Technology (JOINT)
Publisher : Program Sarjana Sistem Informasi, Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/joint.v6i3.11376

Abstract

Perkembangan teknologi informasi menuntut instansi pemerintah untuk menyediakan layanan internal yang efisien, salah satunya melalui sistem helpdesk. Penelitian ini bertujuan menganalisis tingkat kepuasan karyawan terhadap pelayanan helpdesk di Kementerian Luar Negeri Republik Indonesia menggunakan metode Customer Satisfaction Index (CSI). Penelitian ini bersifat kuantitatif dengan pendekatan deskriptif melalui penyebaran kuesioner skala Likert kepada 97 responden yang pernah menggunakan layanan helpdesk. Instrumen penelitian diuji validitas dan reliabilitasnya sebelum dilakukan analisis CSI yang mencakup lima indikator utama: responsivitas, ketepatan solusi, kepuasan pengguna, kejelasan komunikasi, dan keandalan layanan. Hasil penelitian menunjukkan nilai CSI sebesar 80,24% yang termasuk kategori “puas”, mengindikasikan bahwa pelayanan helpdesk telah memenuhi harapan pengguna internal. Temuan ini menegaskan pentingnya keberadaan layanan helpdesk yang responsif dan solutif dalam mendukung efektivitas kerja di lingkungan birokrasi. Kesimpulan dari penelitian ini adalah bahwa kepuasan karyawan terhadap pelayanan helpdesk berada pada tingkat yang baik, namun evaluasi berkelanjutan tetap diperlukan agar kualitas layanan semakin meningkat. Implikasi dari hasil ini adalah perlunya strategi penguatan kapasitas helpdesk sebagai bagian integral dari sistem teknologi informasi kementerian untuk menunjang kinerja diplomasi dan operasional organisasi.
Co-Authors Ade Christian Adi Supriyatna Ali Haidir Andry Maulana Cahya, Titus Dwi Dimas Wahyu Aditya Fauzi Ahmad Muda Felix Wuryo Handono Fernando B Siahaan Ginabila, Ginabila Hafis Nurdin Handi Hidayatullah Hariyanto Hary Sugiarto Ibnu Akil Karlena Indriani Mohammad Badrul Mohammad Badrul, Mohammad Muhammad Iqbal Ningrum, Rina Kusuma Prasetyo, Romadhan Edy Riyandi Al Rino Sumanto, Sumanto Sumarna Sumarna Sumarna Toni Wirawan Tri Budi Haryanto Wirawan, Toni Yosan Erwanto