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All Journal Jurnal Informatika Jurnal Nasional Komputasi dan Teknologi Informasi Media Informatika
Ali Akbar Rismayadi, Ali Akbar
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Neuroscience Transportation

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PERANCANGAN APLIKASI SISTEM PAKAR DIAGNOSA KERUSAKAN HARDWARE KOMPUTER METODE FORWARD CHAINING Rismayadi, Ali Akbar
Jurnal Informatika Vol 3, No 2 (2016): Jurnal INFORMATIKA
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (983.935 KB) | DOI: 10.31294/ji.v3i2.884

Abstract

AbstractDamage to computer hardware, not a big disaster, because not all damage to computer hardware can not be repaired, nearly all computer users, whether public or institutions often suffer various kinds of damage that occurred in the computer hardware it has, and the damage can be caused by various factors that are basically as the user does not know the cause of what makes the computer hardware used damaged. Therefore, it is necessary to build an application that can help users to mendiganosa damage to computer hardware. So that everyone can diagnose the type of hardware damage his computer. Development of expert system diagnosis of damage to computer hardware uses forward chaining method by promoting alisisis descriptive of various damage data obtained from several experts and other sources of literature to reach a conclusion on the diagnosis of damage. As well as using the waterfall model as a model system development, starting from the analysis stage to stage software needs support. This application is built using a programming language tools Eclipse ADT as well as SQLite as its database. diagnosis expert system damage computer hardware is expected to be used as a tool to help find the causes of damage to computer hardware independently without the help of a computer technician.
Perancangan Alat Monitoring Ketinggian Air Bak Berbasis IoT Menggunakan Mikrokontroler Node MCU ESP8266 Rismayadi, Ali Akbar; Sobri, Muhammad Ali; Khoirunnisa, Fitri; Dedy, Asep
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 7, No 4 (2024): Agustus 2024
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v7i4.7852

Abstract

Abstrak - Penelitian ini mengembangkan sistem pemantauan dan kontrol ketinggian air berbasis IoT menggunakan NodeMCU ESP8266 untuk mengoptimalkan penggunaan dan pengelolaan air. Sistem ini memanfaatkan teknologi IoT untuk mengumpulkan dan mengirim data mengenai level air melalui Wi-Fi, serta memungkinkan pengendalian jarak jauh. Dengan menggunakan sensor limit switch dan modul relay, sistem ini mampu memantau dan mengatur aliran air secara otomatis, mengurangi pemborosan, dan memastikan pengelolaan yang efisien. Hasil dari penelitian ini menunjukkan bahwa sistem yang dikembangkan efektif dalam meningkatkan manajemen air dan meminimalisir kesalahan manusia.Kata kunci: Internet of Things (IoT), NodeMCU ESP8266, Monitoring Abstract - This research develops an IoT-based air altitude monitoring and control system using NodeMCU ESP8266 to optimize air use and management. This system utilizes IoT technology to collect and send data about air levels via Wi-Fi, and enables remote control. By using limit switch sensors and relay modules, this system is able to connect and regulate air flow automatically, reducing waste and ensuring efficient management. The results of this research show that the system developed is effective in improving air management and minimizing human error.Keywords: Internet of Things (IoT), NodeMCU ESP8266, Monitoring
Perbandingan Kinerja Metode Machine Learning Support Vector Machine (SVM), Random Forest, dan K-Nearest Neighbors (KNN) dalam Prediksi Harga Saham Apple Rismayadi, Ali Akbar; Febrianto, Rudhi Wahyudi; Raharja, Agung Rachmat; Hariyanti, Ifani
Media Informatika Vol 23 No 3 (2024)
Publisher : P3M STMIK LIKMI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37595/mediainfo.v23i3.299

Abstract

Penelitian ini mengevaluasi kinerja tiga model machine learning SVM, Random Forest, dan KNN untuk memprediksi harga saham Apple. Menggunakan data historis saham, modelmodel tersebut dinilai berdasarkan metrik utama: Mean Squared Error (MSE), Mean Absolute Error (MAE), dan R-Squared (R²). Hasil penelitian menunjukkan bahwa SVM adalah model dengan kinerja terbaik, dengan MSE sebesar 0.2637, MAE sebesar 0.2710, dan R² sebesar 0.9999, yang mencerminkan akurasi prediksi yang sangat baik dan keandalan yang tinggi. Model Random Forest menunjukkan kinerja yang cukup kompetitif dengan MSE sebesar 0.4781, MAE sebesar 0.3852, dan R² sebesar 0.9998. Sebaliknya, model KNN memiliki tingkat kesalahan tertinggi, dengan MSE sebesar 0.7938 dan R² sebesar 0.9997, sehingga kurang cocok untuk dataset ini. Temuan ini menegaskan bahwa SVM adalah model yang paling andal untuk memprediksi harga saham Apple secara akurat. Penelitian ini memberikan wawasan penting dalam pemilihan model pembelajaran mesin untuk prediksi deret waktu finansial, yang dapat mendukung pengambilan keputusan yang lebih baik dalam analisis pasar saham.
Co-Authors Agung Rachmat Raharja Dedy, Asep Febrianto, Rudhi Wahyudi Fitri Khoirunnisa Hariyanti, Ifani Sobri, Muhammad Ali