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All Journal Dinamik Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Riset Informatika Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) The Indonesian Journal of Computer Science
Achmad Ridwan
Universitas Prima Indonesia
Aerospace Engineering Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : The Indonesian Journal of Computer Science

 1 
Pengembangan dan Pemanfaatan Aplikasi Literasi Digital Berbasis Android untuk Meningkatkan Kompetensi Mengajar Guru Amir Saleh; Fadhillah Azmi; Achmad Ridwan; M. Khalil Gibran
The Indonesian Journal of Computer Science Vol. 12 No. 6 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i6.3550

Abstract

Dalam era digital, guru perlu memiliki kompetensi pedagogis, kepribadian, profesional, dan sosial, termasuk kemampuan menggunakan teknologi. Sementara itu, pembelajaran berbasis teknologi di MTs. Al-Hijrah NU Medan belum sepenuhnya dilaksanakan karena berbagai kendala, seperti belum dimanfaatkannya aplikasi literasi digital dengan maksimal. Penelitian ini mengusulkan pengembangan aplikasi literasi digital untuk membantu guru dalam meningkatkan kemampuan mengajar dengan memanfaatkan teknologi dalam pembelajaran. Beberapa kendala yang ada terkait ketersediaan perangkat dan pemahaman guru tentang literasi digital. Pembelajaran literasi digital diperlukan untuk meningkatkan kemampuan guru dalam mengoperasikan teknologi karena hampir semua pembelajaran saat ini menggunakan media digital. Berdasarkan hasil implementasi aplikasi yang telah dikembangkan memperoleh hasil yang cukup baik, dimana memperoleh tingkat kepraktisan produk sebesar 83,13%. Sementara itu, penilaian yang diperoleh dari guru menunjukkan bahwa terdapat peningkatan sebesar 75% pada pengetahuan guru mengenai literasi digital dan peningkatan sebesar 81% pada kemampuan mereka dalam menerapkan literasi digital. Dari hasil perolehan nilai-nilai tersebut menyatakan bahwa pengembangan aplikasi yang dilakukan terbukti efektif dan mampu meningkatkan kemampuan mengajar guru.
Enhancing Water Potability Assessment Using Hybrid Fuzzy-Naïve Bayes Azmi, Fadhillah; Gibran, M Khalil; Ridwan, Achmad
The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i3.3232

Abstract

In an effort to ensure a safe and high-quality water supply, the assessment of water potability is of paramount importance. An accurate and efficient assessment of water potability can be a challenge due to various influencing factors. Therefore, an innovative and integrated approach is needed to improve the assessment of water potability. In this study, we introduce a new approach to improving the assessment of water potability. This approach aims to overcome the shortcomings of traditional methods by using a hybrid fuzzy-Naïve Bayes approach to obtain a more accurate level of water potability. Fuzzy techniques are used to overcome uncertainty and ambiguity in the initial data. This method describes the probability weights in a fuzzy manner for various parameters. Then, the Naïve Bayes method is used to classify water samples based on the probability generated by the fuzzy system. This hybrid approach makes it possible to consider the relationship between parameters and generate more realistic probability values. This study uses datasets collected from various sources that include water potability parameters. A hybrid fuzzy-Naïve Bayes approach was then applied to this data set to make a more effective and accurate assessment of water potability. The experimental results show that the proposed method obtains an accuracy of 90%, which significantly improves the water potability assessment compared to the conventional method, which results in an accuracy of 84%. By combining fuzzy and Naïve Bayes techniques, we can overcome uncertainty in data and produce more accurate judgments.
Machine Learning and Fuzzy C-Means Clustering for the Identification of Tomato Diseases Saleh, Amir; Ridwan, Achmad; Gibran, M Khalil
The Indonesian Journal of Computer Science Vol. 12 No. 5 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i5.3379

Abstract

Diseases in tomato plants can cause economic losses in the agricultural industry. Identification of tomato plant diseases is important to choosing the right action to control their spread. In this research, we propose an approach to identify tomato plant diseases using a machine learning algorithm and lab colour space-based image segmentation using the fuzzy c-means (FCM) clustering algorithm. The segmentation method aims to separate the infected area, leaf image, and background in the tomato plant image. In the first step, the tomato image is represented in the Lab colour space, which allows for combining information on brightness (L), red-green colour components (a), and yellow-blue colour components (b). Then, the FCM algorithm is applied to segment the image. The segmentation results are then evaluated through an identification process using machine learning techniques such as k-Nearest Neighbors (kNN), Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB) to measure the level of accuracy. The dataset used in this research is tomato images, which include various plant diseases obtained from the Kaggle dataset. The performance results of the proposed method show that the segmentation approach based on Lab colour space with the FCM clustering algorithm is able to identify infected areas well. The accuracy value of each machine learning method used is kNN of 85.40%, RF of 88.87%, SVM of 80.73%, and NB of 74.60%. The proposed method shows success in accurately identifying types of tomato plant diseases and obtains improvements compared to without using segmentation.
Oven Listrik Keripik Buah Berbasis Arduino dengan Menggunakan Metode Fuzzy Logic dan Sensor DHT22 Achmad Ridwan
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4382

Abstract

Small-scale fruit chip production faces challenges in maintaining optimal temperature and humidity during the drying process. This research develops the Endull Kripps electric oven using Arduino-based fuzzy control technology and DHT22 sensors to address these issues. The aim is to design an efficient oven system that produces high-quality chips. The Research and Development method was applied in system design and testing. Results show up to 95% increase in energy efficiency compared to conventional methods. The chips produced have 1.5% lower moisture content, crispier texture, and higher organoleptic scores. The system maintains temperature stability with high accuracy, demonstrating significant potential in optimizing food drying processes. This innovation supports the Merdeka Belajar Kampus Merdeka program by providing a platform for students to develop entrepreneurial skills in the context of the food industry.
Co-Authors Agung Prabowo Amir Saleh Duandes, Alfonso Fadhillah Azmi Fadhillah Azmi Fadhillah Azmi, Fadhillah Gibran, M Khalil Hutagalung, Elia Yose Mayal Lenny, Lenny M Khalil Gibran M. Khalil Gibran Nugraha, R. Ferdy Akbar Segono, Yohandres Sihombing, Yehezkiel Simangunsong, Putra Torang Simbolon, Harly Gumanti Susena, Arya Tindaon, Novendy Alberto Will Us, Nurul Iman Violyn, Violyn Yennimar, Yennimar