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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Tematik : Jurnal Teknologi Informasi Komunikasi Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Innovative: Journal Of Social Science Research International Journal of Sustainable Applied Sciences (IJSAS) Jurnal Ilmiah Informatika dan Komputer International Journal of Integrated Science and Technology
Muhammad Fatchan
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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ANALISIS EFISIENSI PEMAKAIAN GAS PADA INDUSTRI KERAMIK Tri Ngudi Wiyatno; Muhammad Fatchan; Ikhsan Romli; Andre Amara
Jurnal Pelita Teknologi Vol 14 No 2 (2019): September 2019
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (293.697 KB) | DOI: 10.37366/pelitatekno.v14i2.236

Abstract

Abstrak – Petumbuhan Industri yang semakin pesat menjadikan pemakaian gas untuk kebutuhan proses produksi juga meningkat, sehingga apabila hal ini tidak diantsipasi maka akan terjadi krisis gas industi, untuk mengantisipasi terjadinya krisis kebutuhan gas industri maka salah satunya diperlukan adanya strategi peningkatan efisiensi dalam pemakaian gas. Pada penelitian ini dilakukan analisis efisiensi pemakaian gas pada Industri keramik, selama proses pembakaran keramik yang ada di tungku Kiln. Pada penelitian ini pengambilan data dilakukan dengan menggunakan data primer yang mengambil langsung dilapangan dan data sekunder dengan cara mengambil data yang sudah ada sebelumnya, selanjutnya dengan data-data tersebut dapat dihitung tingkat efisiensi gas yang dipakai untuk proses produksi. Dengan diketahuinya efisinsi gas yang digunakan dalam pembakaran keramik diharapkan dapat membantu untuk dilakuannya analisa perbaikan guna meningkatkan efisiensi pemakaian gas pada proses pembakaran keramik. Dari hasil penelitian didapat nilai efisiensi Kiln pada PT.XYZ selama bulan April 2019 sebesar 77,68% Kata kunci : gas, kiln, efisiensi.
Sistem Informasi Inventaris Peralatan Berbasis Web Pada Yayasan Pendidikan Islam Al-Azni Muhammad Fatchan; Novi Sri Yuliani; Andri Firmansyah
JIIFKOM (Jurnal Ilmiah Informatika dan Komputer) Vol 1 No 1 (2022): January
Publisher : Jurusan Informatika STTR Cepu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51901/jiifkom.v1i1.217

Abstract

Inventory equipment is one of the facilities provided to support and support the process of teaching and learning activities for teachers at the Al-Azni Islamic Education Foundation. However, currently the management of inventory data is not well organized so that it affects the process of borrowing and returning inventory equipment, because it is still not computerized, that is, the borrower only needs to write data on borrowing and returning goods in a notebook, this can have an impact on the delay in the work process if a lot of goods are needed. borrowed and returned. and the risk of goods being lost or damaged without the origin of the last borrower who used it. The purpose of this thesis is to produce a web-based equipment inventory information system with PHP and MySQL. The test is carried out using the Black-box testing method. From the results of the study it can be concluded that the existence of a web-based inventory information system can accelerate data processing about the facilities and infrastructure owned by the agency, and also has the ease and accuracy of running the system at the Al-Azni Islamic Education Foundation
Aplikasi Sistem Informasi Jasa Make Up Berbasis Web Pada Studi Kasus IYAIYOH Make Up Muhammad Fatchan; Rohayati Rohayati
JIIFKOM (Jurnal Ilmiah Informatika dan Komputer) Vol 1 No 2 (2022): July
Publisher : Jurusan Informatika STTR Cepu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51901/jiifkom.v1i2.241

Abstract

Make Up Artists (MUA) usually offer their services using social media such as Facebook and Instagram. However, through the media, it is still not optimal because it is difficult for customers to know the location and whether the make-up service can come directly to the customer's place or not. In the ordering process, lyaiyoh Make Up is still done manually, that is, it is recorded in a book and the customer must contact by telephone or come directly to the salon to place an order. The purpose of this research is to design a web-based make-up service information system that was built using the Waterfall method. The benefits of this research are making it easier to register and ordering for customers, more structured and directed data management so as to minimize errors in recording. The conclusion is that in addition to the data processing process that becomes computerized, this system can also be a promotional media for lyaiyoh Make Up services
CNN Algorithm Approach for Classification of Tomato Fruit Maturity Levels Taufik Hidayat; Muhammad Fatchan; Wahyu Hadikristanto
International Journal of Sustainable Applied Sciences Vol. 2 No. 5 (2024): May 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijsas.v2i5.1862

Abstract

The categorization of tomato maturity is covered in this study, which has important ramifications for the food sector and agriculture. For training efficiency, the approach uses augmentation with adjustments to rescale picture pixel values and shrink image sizes. According to the experiment's findings, accuracy increased by 93% throughout five training epochs. The training and validation graph indicates steady progress, despite the lack of significance in the improvement. Misclassifications that require correction are found during evaluation utilizing the confusion matrix. The study emphasizes that to enhance agricultural production management, flaws in the model must be filled and accuracy must be increased. The amount and diversity of photos in the dataset should be increased, as should the shooting angles and lighting conditions, and hyperparameters should be adjusted for future model performance optimization.
Analisis Sentimen Twitter Terpilihnya Prabowo - Gibran Menggunakan Metode Neural Network Diana Dwi Rahayu; Muhammad Fatchan; Alfonsus Ligouri
TEMATIK Vol. 11 No. 1 (2024): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2024
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v11i1.1943

Abstract

One of the most important elections in Indonesian democracy is the presidential election, which chooses the country's leader for the next five years. In the 2024 presidential election, there are three candidates for president and vice president, including the Prabowo Subianto - Gibran Rakabuming Raka pair. The election process has taken centre stage on social media, particularly Twitter, where people interact, share information, and express their opinions and feelings. This study aims to look at public opinion towards the Prabowo-Gibran team, which has attracted a lot of attention since Gibran was nominated as a vice presidential candidate until he was declared the winner in the 2024 presidential election by the KPU. This analysis provides valuable insight into understanding public opinion and feelings towards the president and vice president-elect. The method used in this research is neural network (NN), which is proven to be effective in text data classification and capable of producing high accuracy. The dataset used is public opinion on Twitter, which is taken through the data crawling process. The initial data of 1511 tweets was then cleaned and prepared into a dataset of 1500 tweets, with the main attribute being the content of the tweet. Based on the findings, the neural network model created was able to classify the sentiment of tweets related to the Prabowo-Gibran pair with an accuracy rate of 93%. Thus, this sentiment analysis makes an important contribution to understanding the public's response to the presidential election process and the election of a new president and vice president
Eye Disease Detection and Classification Optimization Using EfficientNet-B5 with Emphasis on Data Augmentation and Fine-Tuning Anggi Muhammad Rifai; Muhammad Fatchan; Ahmad Turmudi Zy; Donny Maulana; Sufajar Butsianto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6519

Abstract

Eye diseases such as glaucoma, cataract, and diabetic retinopathy pose significant global health challenges, underscoring the need for accurate and efficient diagnostic systems. This study employed the EfficientNet-B5 model to enhance the detection and classification of eye diseases by incorporating advanced data augmentation and fine-tuning techniques. The research utilizes the Ocular Disease Intelligent Recognition (ODIR) dataset, consisting of 4,217 fundus images categorized into four classes: normal, glaucoma, cataract, and diabetic retinopathy. The methodology comprises three phases: baseline model training, model training with data augmentation, and fine-tuning. The baseline model achieved an accuracy of 60.43%, which improved to 63.03% with data augmentation—an increase of 2.6 percentage points. Fine-tuning further elevated the accuracy to 93.23%, representing a notable improvement of 33.8 percentage points over the baseline. Model performance was evaluated using standard classification metrics including accuracy, precision, recall, and F1-score. These findings demonstrate the technical efficacy of combining augmentation and fine-tuning to enhance model generalization. The proposed approach offers a robust framework for developing dependable AI-driven diagnostic tools to support early detection and facilitate informed clinical decision-making.
Analisa Klasifikasi Tingkat Kelulusan Mahasiswa Metode Algoritma Naïve Bayes Menggunakan Rapidminer Firmansyah, Agung; Muhammad Fatchan; Rifa’i, Anggi Muhammad
Innovative: Journal Of Social Science Research Vol. 4 No. 6 (2024): 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.v4i6.16965

Abstract

Perkembangan teknologi informasi menghasilkan data besar dari berbagai bidang, termasuk pendidikan. Data mining membantu menemukan pola dalam dataset agar lebih bermanfaat. Di perguruan tinggi, kelulusan tepat waktu menjadi indikator kinerja, tetapi banyak mahasiswa terlambat lulus karena faktor seperti keuangan, pekerjaan, dan kurangnya tanggung jawab. Penelitian ini menggunakan Naive Bayes Classifier untuk memprediksi kelulusan mahasiswa dan merekomendasikan langkah yang diperlukan. Analisis dengan perhitungan manual, Microsoft Excel, dan RapidMiner menunjukkan akurasi 93,33%, precision 100%, dan recall 66,67%, membuktikan metode ini efektif untuk klasifikasi data tingkat kelulusan.
Classification of Drinking Water Potability With Artificial Neural Network Algorithm Darmawan, Indra; Muhammad Fatchan; Andri Firmansyah
International Journal of Integrated Science and Technology Vol. 2 No. 5 (2024): May 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijist.v2i5.1874

Abstract

Having safe water for consumption is essential for public health in every region. However, water quality is declining in some places, especially to meet human needs for drinking water. There are many efforts to maintain water potability, such as checking to see if there are bacteria or diseases in the water. This research classifies water potability using the Artificial Neural Network method, a technique in the field of machine learning. This research classifies water quality using a python library to analyze data and perform classification. Data is processed through stages such as data cleaning and data division into training and testing. In testing, the data is divided into 20% for testing and 80% for training. The results of the ANN algorithm show 70% accuracy. in conclusion, the ANN model has moderate performance in classifying the feasibility of drinking water. Model improvement is needed to improve accuracy and prediction, including the use of larger and more diverse datasets.
Advanced ANN Techniques for Precise Detection and Classification of Welding Defects Faza Ardan Kusuma; Muhammad Fatchan; Ahmad Turmudi Zy
International Journal of Integrated Science and Technology Vol. 2 No. 5 (2024): May 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijist.v2i5.1907

Abstract

The implementation of the artificial neural network (ANN) algorithm for detecting and classifying welding defects is detailed in this study. A total of 558 welding workpiece images were processed using techniques such as resizing, auto-orientation, flipping, rotation, and annotation, ultimately expanding the dataset to 1,288 images. Feature extraction identified 24 traits across 12,000 data points, which were then condensed to 5,735 data points for the ANN model. The model employed 100 hidden layers, the ReLU activation function, and the L-BFGS-B solver, running for 200 iterations. The configuration achieved near-perfect results, with metrics such as the area under the curve (AUC), classification accuracy, and F1 score averaging a precision of 0.97. These outcomes demonstrate the ANN model's high efficacy in detecting and classifying welding defects, underscoring its potential application for quality assurance in the welding industry. Further investigation into specific defect types, including porosity, spatter, cracks, and undercuts, could further improve detection accuracy.
Co-Authors Agung Firmansyah Ahmad Turmudi Zy Alfonsus Ligouri Andre Amara Andri Firmansyah Anggi Muhammad Rifa'i Anggi Muhammad Rifai Butsianto, Sufajar Darmawan, Indra Diana Dwi Rahayu Donny Maulana Faza Ardan Kusuma Ikhsan Romli Novi Sri Yuliani Rohayati Rohayati Taufik Hidayat Tri Ngudi Wiyatno Wahyu Hadikristanto