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All Journal Jurnal Informatika Jurnal Teknologi Sistem Informasi dan Aplikasi BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer
Rayadin, Muhamad Amhar
Unknown Affiliation
Computer Science & IT

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Perbandingan Efisiensi Deteksi Tepi Roberts, Prewitt, dan Canny untuk Identifikasi Kartu Mahasiswa Saputra, Rizal Adi; Rayadin, Muhamad Amhar; Febryanti, Wa Ode Ika
Jurnal Informatika Vol 10, No 2 (2023): October 2023
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v10i2.16726

Abstract

Sekarang ini setiap universitas/perguruan tinggi memiliki identitas tersendiri yang membedakannya dengan universitas/perguruan tinggi lain. Memisahkan mahasiswa dari universitas berdasarkan kartu mahasiswa mereka.  Dalam kartu mahasiswa terdapat data NIM. Nomor Induk Mahasiswa (NIM) adalah nomor yang digunakan sebagai nomor identitas mahasiswa selama masa studi berlangsung. Berdasarkan hal itu sebuah perguruan tinggi perlu memiliki sistem yang dapat mengidentifikasi NIM pada kartu mahasiswa. Adanya sistem tersebut dapat mempermudah dalam mengidentifikasi data mahasiswa berdasarkan nim yang tertera pada kartu mahasiswa. Untuk melakukan identifikasi data NIM pada kartu mahasiswa, dapat dilakukan metode deteksi tepi. Operator kernel pertama yang digunakan adalah Roberts. Kemudian digunakan Prewitt, lalu terakhir Canny. Ketiga metode ini bekerja dengan mengidentifikasi dan menonjolkan lokasi pixel yang memiliki nilai perbedaan intensitas citra yang ekstrim, akan tetapi ketiganya memiliki perbedaan pada ukuran kernel, kompleksitas metode, dan sensivitas terhadap derau. Penelitian ini bertujuan untuk membandingkan efisiensi antara ketiga operator tersebut dalam deteksi tepi. Dari hasil identifikasi NIM, diperoleh nilai rata - rata MSE, RMSE, dan PSNR operator Canny dari data uji yaitu  0.34068692, 0.57071118 dan 53.08796. Nilai ini adalah lebih baik dibanding dengan Roberts dan Prewitt. Dengan demikian dapat disimpulkan bahwa operator Canny adalah yang terbaik untuk melakukan deteksi tepi pada kartu mahasiswa
Implementasi Ensemble Learning Metode XGBoost dan Random Forest untuk Prediksi Waktu Penggantian Baterai Aki Rayadin, Muhamad Amhar; Musaruddin, Mustarum; Saputra, Rizal Adi; Isnawaty, Isnawaty
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 5 No 2 (2024): September
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v5i2.128

Abstract

In motor vehicles, including cars, the battery plays an important role, namely as a place to store electrical energy and as an electric voltage stabilizer when the engine is turned on. In general, motorized vehicle users do not know the condition of the battery in their vehicle. Even though the use of battery batteries that are already in poor condition can interfere with vehicle performance. In battery replacement services such as after-sales service, the process of checking and replacing battery batteries takes a relatively long time. This can be caused by high service volume, lack of worker reliability, lack of responsiveness to the complexity of the inspection. This research aims to build a prediction model for battery battery replacement time quickly. To meet these needs, a Machine Learning approach can be used. Machine Learning uses historical replacement data to make predictions of replacement time. Machine Learning algorithms that can be used for prediction are XGBoost and Random Forest. This research uses ensemble learning techniques to combine the two models. Based on the evaluation results, it can be concluded that the model built with ensemble learning has better prediction results than a single model. Evaluation results with MSE on the ensemble bagging model have the lowest error values of 145,448. The MAPE, MAE, and RMSE evaluations on the ensemble boosting model have the lowest error values of 11.56 %, 43.80 and 38,760.
Pengembangan Sistem Pemantauan Perkuliahan Jurusan Teknik Informatika Universitas Halu Oleo Berbasis Website dengan Agile Development Methods Rayadin, Muhamad Amhar; Billa, Nabilla Salsa; Thalib, La Ode Jafar Umar; Saputra, Rizal Adi
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 1 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i1.35188

Abstract

To keep up with the ongoing progress in information and communication technology, the administration of academic activities at the Department of Informatics Engineering at Halu Oleo University must adjust its methods to ensure optimal efficiency and precision. An important aspect is the monitoring of lectures, which now depends on a manual approach, such as traditional methods of documenting attendance. Common problems include extended data processing time, vulnerability to data loss, and frequent recording errors. To tackle these difficulties, this work utilizes the Agile Extreme Programming approach to construct a web-based lecture monitoring system. The system is designed to streamline the process of recording and tracking professor attendance by engaging all pertinent stakeholders, such as class coordinators, lecturers, and administrators. The system offers versatility to students and professors in data management, while also allowing administrators to oversee course information, faculty members, students, and academic terms. The adoption of the Agile Extreme Programming methodology in the development of this web-based lecture monitoring system has effectively addressed the efficiency issues encountered in the lecture monitoring process at the Department of Informatics Engineering, Halu Oleo University. Adopting a web-based system has enhanced the efficiency of academic activity management by replacing manual signature-based attendance sheets. This transition has facilitated the recording of lecturer attendance and the monitoring of lectures. The research findings underwent evaluation through the black box testing methodology and produced outcomes consistent with anticipated outcomes.
Co-Authors Billa, Nabilla Salsa Febryanti, Wa Ode Ika Isnawaty Isnawaty, Isnawaty Mustarum Musaruddin Rizal Adi Saputra Thalib, La Ode Jafar Umar