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All Journal BERKALA FISIKA JURNAL SISTEM INFORMASI BISNIS YOUNGSTER PHYSICS JOURNAL Jurnal Sains dan Teknologi Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Physics and Its Applications Jurnal Sisfokom (Sistem Informasi dan Komputer) Journal of Information Systems and Informatics Jurnal Fisika Indonesia JINAV: Journal of Information and Visualization Journal of Applied Data Sciences Jurnal Algoritma Journal of Emerging Science and Engineering Scientific Journal of Informatics

Catur Edi Widodo

Departemen Fisika, Fakultas Sains Dan Matematika, Universitas Diponegoro, Semarang

Author-ID : 3136302

Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Materials Science & Nanotechnology Mathematics Physics

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Simulator input-output sistem kontrol menggunakan Raspberry Pi Zainal Bachrudin; Catur Edi Widodo; Kusworo Adi
Youngster Physics Journal Vol 6, No 3 (2017): Youngster Physics Journal Juli 2017
Publisher : Jurusan Fisika, Fakultas Sains dan Matematika, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (892.233 KB)

In this research has been made I / O simulator which is a tool to simulate input and output of a control system using Raspberry Pi. Raspberry Pi has 26 GPIO (General Purpose Input and Output) pins that can be used to control inputs and outputs on the I / O simulator. The 26 GPIO pins are divided into two main systems, is 13 GPIO pins that are odd numbered as inputs and 13 other GPIO pins which are even numbered as outputs. The Raspberry Pi GPIO pins are ordered as inputs and outputs using Python programming languages. The command is done by reading the switch as input signal input, then Raspberry Pi process the input signal and send data as output signal with LED flame on the I / O Simulator. The I / O simulator can simulate logic gates, as AND, OR, NOT, and ADD, and can run mini distillation plant.Keywords: Simulation, Input-Output, Raspberry Pi, Python

Prototype sistem pakar diagnosis penyakit diabetes Catur Edi Widodo
Youngster Physics Journal Vol 6, No 2 (2017): Youngster Physics Journal April 2017
Publisher : Jurusan Fisika, Fakultas Sains dan Matematika, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (118.633 KB)

Every member of the community can experience a variety of diseases. The disease can be known from the symptoms it produces, but to know the exact type of disease, needed a doctor or a health professional. Since the number of doctors or health professionals is very limited and can not overcome the problems of the community at the same time, a system that has the capability of a doctor or health professional is required, which in this system contains the expertise of a physician or health professional on diseases and diseases. In this study was designed expert system using rule base (reason based reasoning) with forward chaining and backward chaining inference method that is intended to assist the community in diagnosing the disease. This disease diagnostic expert system developed has advantages in ease of access and ease of use. With the features that are owned, expert systems for the diagnosis of diseases that built can be used as a tool for disease diagnosis and can be accessed by the public to overcome the problem of limited number of doctors or health experts in helping people diagnose the disease.Keywords: disease, expert system, backward chaining, forward chaining, rule-based reasoning

Design of Automatic Bottle Filling Using Raspberry Pi Hadyan Arifianto; Kusworo Adi; Catur Edi Widodo
Journal of Physics and Its Applications Vol 1, No 1 (2018): November 2018
Publisher : Diponegoro University Semarang Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jpa.v1i1.3910

Water consumption is very high, especially in urban areas. This means a good business opportunity for small and medium enterprises. Those enterprises, therefore, require an automatic and affordable device that can fill water into bottles. Raspberry Pi is the center of the control system in designing this automatic bottle filling device. This is because Raspberry Pi comes a with GPIO pin that is used as an input-output controller. GPIO pin receives signal input from switches and sensors that are then processed using Python programming language to drive an actuator and a solenoid valve. Subsequent hardware testing includes tests for water sensor, director motor, alternating motor, and solenoid valve. It is found that the water sensor works at a voltage of 4.18 V and that The DC motor works at 13.92 V. It is also found that the DC motor moves back and forth at 34.77 V when it is moving up, and at -34.77 V, when it is moving down. Meanwhile, the solenoid valve is found to work at 224.9 V. Therefore; it’s very possible to use Raspberry Pi as the center of a control system for an automatic bottle filling device.

Pengaruh Jumlah Iterasi dan Nilai Parameter Relaksasi Terhadap Signal to Noise Ratio (SNR) pada Rekonstruksi Citra Metode SIRT (Halaman 13 s.d. 16) Choirul Anam; Catur Edi Widodo
Jurnal Fisika Indonesia Vol 17, No 51 (2013)
Publisher : Department of Physics Universitas Gadjah Mada

Kualitas citra hasil rekonstruksi metode Simultaneous Iterative Reconstruction Technique (SIRT) ditentukan oleh jumlah iterasi dan nilai parameter relaksasi (λ). Nilai λ ini biasanya diperoleh secara coba-coba. Penelitian ini bertujuan mengevaluasi pengaruh jumlah iterasi dan nilai λ terhadap nilai signal to noise ratio (SNR). Rekonstruksi menggunakan obyek fantom Shepp-Logan ukuran 50x50. Proyeksi dilakukan untuk setiap sudut 100menggunakan mode berkas paralel. Pertama ditentukan pengaruh jumlah iterasi terhadap SNR. Selanjutnya dilakukan penentuan nilai SNR untuk variasi λ pada jumlah iterasi tertentu. Diperoleh bahwa semakin banyak iterasi dan semakin besar nilai parameter relaksasi menghasilkan nilai SNR semakin tinggi, namun setelah iterasi dan nilai λ tertentu, nilai SNR mengalami saturasi. Citra dengan kualitas optimal (kekaburan dan stripping paling kecil), diperoleh pada iterasi antara 5 hingga 10 dan nilai parameter relaksasi antara 0,5 hingga 1.

Shrimp and fish underwater image classification using features extraction and machine learning Setiawan, Arif; Hadiyanto, H.; Widodo, Catur Edi
Journal of Emerging Science and Engineering Vol. 2 No. 1 (2024)
Publisher : BIORE Scientia Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/jese.2024.e14

Shrimp cultivation is one type of cultivation that has a significant impact on the social status of coastal communities. Shrimp farming traditionally faces several challenges, including water pollution, imbalances in temperature, feed, media, and costs. Monitoring the condition of shrimp in the cultivation environment is very necessary to determine the condition of shrimp in the water. Classification of shrimp and fish is the first step in monitoring the condition of shrimp underwater. This research proposes the development of a method for classifying shrimp and fish underwater using feature extraction and machine learning. The flow of this research is: (1) preparing data from ROI detection results, (2) extraction process of morphometric characteristics P and T, (3) calculating the value of morphometric characteristics P and T, (4) data breakdown for training data and testing data, (5) Model creation process, data training and data testing using SVM, RF, DT, and KNN, (6) Evaluation of classification results using a confusion matrix. From this research, it was found that the Random Forest method obtained the highest accuracy, namely 0.93. From this matrix, the values obtained are True Positive = 349, False Positive = 28, True Negative = 223, False Negative = 0.

Trend Analysis of The Effect of LQ45 Stocks on Stock Price Index Fluctuations using the C4.5 Algorithm with Correlation-Based Feature Selection and Information Gain Fitra Nur Asri, Muh; Edi Widodo, Catur; Sediyono, Eko
JINAV: Journal of Information and Visualization Vol. 4 No. 2 (2023)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1836

The research was conducted to reveal the effect of LQ45 stock on the accuracy of stock price index fluctuations using the C4.5 algorithm with Correlation-Based Feature Selection (CFS) and Information Gain (IG) techniques. This study used the superior C4.5 algorithm using a combination feature selection technique between Correlation-based Feature Selection (CFS) and Information Gain in the hope of getting accurate results. Analysis conducted on the LQ45 index through various stages that include data collection, manual pre-processing, validation methods, process features, decision tree model result, and classification accuracy performance. The result of test revealed that the implementation of the C4.5 algorithm using correlation-based feature selection (CFS) and information gain techniques can be applied well to LQ45 stocks. The accuracy generated from the original data (without the selection feature) was 77.857%, while the addition of features to the combination of Correlation-Based Feature Selection (CFS) and Information Gain had a large influence on the results of increasing data accuracy from the accuracy of the original data by 77.857% to 78.333%. Thus, the C4.5 calculation process with the Correlation-based Feature Selection (CFS) feature selection technique alone cannot improve the accuracy level, while when combined with the Information Gain technique, the accuracy processing results will be better (higher).

Sistem Informasi Uji Forensik Proses Klasterisasi Protektil Amunisi Senjata Api Menggunakan Algoritma Gray Level Co-occurance Matrix dan K-Mean Clustering Supriyadi, Didik; Widodo, Catur Edi; Isnanto, R. Rizal
Jurnal Algoritma Vol 21 No 2 (2024): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.21-2.2119

Pemanfaatan teknologi menjadi solusi saat perkembangan jaman terus meningkat dan berkembang. Tidak terkecuali keterkaitan teknologi untuk bidang keamanan negara. Metode yang mendukung klaterisasi adalah ekstraksi ciri menggunakan Gray Level Co-occurence Matrix (GLCM) yang dilakukan sebelum proses klaterisasi itu sendiri. GLCM sangat cocok digunakan untuk melakukan ekstraksi fitur atau ciri-ciri pada citra yang memiliki pola-pola khusus seperti penelitian pengenalan pola wayang. Prosedur penelitian ini merupakan alur dari flowchat untuk membangun sistem informasi untuk uji forensik proses klasterisasi proyektil amunisi senjata api menggunakan algoritma Gray Level Co-occurrene Matrices (GLCM) dan K-Means clustering. Pada Gambar 3.1 berikut merupakan kerangka sistem informasi sebagai penjelas setiap alur input, proses dan output diilustrasikan.Hasil penelitian menunjukkan bahwa penggunaan metode GLCM sebagai ekstraksi fitur dari image grayscale dan metode K-Means untuk clustering memberikan hasil dan akurasi yang cukup baik. Performa model mencapai 71.14% meski dengan keterbatasan data yang dimiliki. Model tersebut dapat digunakan tidak hanya pada aplikasi console seperti Google Collabs, tetapi juga dapat digunakan pada aplikasi yang memiliki GUI dengan performa aplikasi yang cukup stabil.

Optimization of Coronary Heart Disease Risk Prediction Using Extreme Learning Machine Algorithm (Case Study: Patients of Dr. Soeselo Hospital) Iswanti, Arie; Isnanto, R. Rizal; Widodo, Catur Edi
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.24746

Purpose: Coronary heart disease (CHD) is the leading cause of death globally, with 17.8 million deaths reported by the WHO in 2021. Early detection remains a major challenge due to low public awareness and dependence on manual diagnostic procedures. These limitations necessitate the development of automated and accurate predictive models. This study aims to construct a CHD risk prediction model using the Extreme Learning Machine (ELM) algorithm. The research addresses a critical limitation in existing models, namely, poor performance on minority classes (CHD stages 2–4), caused by data imbalance. To overcome this, oversampling techniques such as Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) are applied. The objective is to improve classification performance, particularly in high-risk categories, and to enhance the model’s generalisation capability for real-world implementation. Methods: This research implements the Extreme Learning Machine (ELM) algorithm to achieve optimal prediction results. The data used in this study as the initial database of patients consists of gender, age, height, weight, whether they have diabetes or not, the number of cigarettes consumed daily, and blood pressure. The data will be the main component in building the heart disease prediction system. The prediction classes are: no heart disease, stage 1 heart disease, stage 2 heart disease, stage 3 heart disease, and stage 4 heart disease. The total number of dataset are 521 data points, with 70% of the training data amounting to 364 patients, and 30% of the test data amounting to 157 patients. The data collection process uses patient data from RSUD Dr. Soeselo, Tegal Regency, Central Java, for the years 2023 and 2024. Result: The research successfully developed and evaluated an Extreme Learning Machine (ELM) algorithm for Coronary Heart Disease (CHD) risk prediction using patient data from Dr. Soeselo Hospital. The model achieved an overall accuracy of 82% on the dataset of 157 patients, demonstrating a promising capability for automated risk assessment. Novelty: This predictive model can be utilised in the medical field to facilitate the early detection of heart disease or other risks. This model will soon be introduced in hospitals in the Tegal Regency and City area, Central Java.

Analisis Sentimen Berbasis Aspek Pada Aplikasi Elektronik Survei Kepuasan Masyarakat (E-SKM) Jawa Tengah Menggunakan Indobert Labib Mustofa, Refo; Labib Mustofa, Tarno; Edi Widodo, Catur
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 4: Agustus 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.124

Perkembangan tentang Natural Language Processing (NLP) semakin berkembang dengan pesat, salah satunya yaitu dalam bidang analisis sentimen. Dalam dunia bisnis, analisis sentimen sangat diperlukan untuk mengetahui dan memahami persepsi pelanggan terhadap produk yang telah didapatkan dari perusahaan. Hal yang sama juga berlaku pada sektor pemerintahan. Pemerintah sebagai penyelenggara pelayanan publik harus dapat mengetahui persepsi dari pengguna layanan terhadap penyelenggaraan pelayanan publik tersebut sebagai bahan perbaikan kualitas layanan. Aplikasi E-SKM merupakan aplikasi milik Pemerintah Provinsi Jawa Tengah yang saat ini hanya mengolah nilai survei layanan meliputi sembilan aspek pertanyaan, sedangkan data saran/masukan pada aplikasi ini belum dimanfaatkan lebih lanjut. Pada penelitian ini, dilakukan analisis sentimen pada data saran/masukan tersebut untuk menggali informasi tambahan yang dapat meningkatkan pemahaman pemerintah terhadap kepuasan pengguna layanan. Metode yang diusulkan yaitu menggunakan pendekatan analisis sentimen berbasis aspek menggunakan model IndoBERT. Pendekatan berbasis aspek ditujukan agar dapat diketahui aspek apa saja yang paling banyak dibicarakan oleh pengguna layanan, terutama yang berhubungan dengan sembilan aspek pertanyaan tersebut. Pada penelitian ini juga digunakan kamus leksikon sebagai pelabelan data, kemudian pendekatan berbasis aturan (rule-based) digunakan dalam proses klasifikasi aspek yang berkaitan dengan sembilan aspek pertanyaan. Selain itu, penelitian ini bertujuan untuk mengukur kemampuan model IndoBERT dalam proses klasifikasi sentimen dengan beberapa skenario yang berbeda. Dari hasil analisis, model evaluasi IndoBERT berjalan dengan baik. Hal ini dilihat dari nilai rata-rata parameter evaluasi seperti akurasi, precision, recall, dan f1-score mencapai 95%. Penerapan model ini memiliki kontribusi pada data aplikasi E-SKM untuk mendapatkan informasi sentimen dan aspek pada data pelayanan publik di pemerintahan yang dapat digunakan sebagai bahan pengambilan keputusan pada level manajemen kebijakan. Abstract The field of Natural Language Processing (NLP) is rapidly advancing, particularly in sentiment analysis. In the business world, sentiment analysis is essential for understanding customer perceptions of products they have received from a company. The same applies to the government sector, where it is crucial for public service providers to gain insight into user perceptions of public services as a basis for service improvement. The E-SKM application, owned by the Central Java provincial government, currently processes only service survey scores covering nine question aspects, while suggestions/feedback data from this application have not yet been fully utilized. In this study, sentiment analysis was conducted on the suggestion/feedback data to extract additional insights that could improve understanding of user satisfaction. The proposed method involves an aspect-based sentiment analysis approach using the IndoBERT model. This aspect-based approach aims to identify the aspects most frequently mentioned by service users, particularly those related to the nine survey aspects. A lexicon-based approach was used for data labeling, followed by a rule-based approach for classifying aspects associated with the nine questions. Additionally, this study aims to assess the performance of the IndoBERT model in sentiment classification across several scenarios. Evaluation results indicate that IndoBERT performs well, with average metrics such as accuracy, precision, recall, and F1-score reaching 95%. The implementation of this model contributes to the E-SKM application data by providing sentiment and aspect information on public service data within the government, which can be used as a basis for decision-making at the policy management level.

Co-Authors Agung Wibowo, Mochamad Agusta Rakhmat Taufani Agustiyar, Agustiyar Aprilia Ayu Andarinny Arif Setiawan Aris Puji Widodo Aryasa, Jiyestha Aji Dharma Budi Warsito Choirul Anam AM Diponegoro Didik Supriyadi, Didik eka rahmawati Eko Sediyono ellya helmud, ellya Faisal Akbar Fatkhur Rohman Figur Humani Fitra Nur Asri, Muh Fitro, Achmad H Hadiyanto Hadiyanto Hadiyanto Hadyan Arifianto Husen Nasrullah Armin I Gusti Bagus Wiksuana Irwan Agus Saputro Isnain Gunadi Iswanti, Arie Jatmiko Endro Suseno K. Sofjan Firdausi Koesuma, Sorja Kusworo Adi Kuworo Adi Labib Mustofa, Refo Labib Mustofa, Tarno Maratullatifah, Yulaikha Muchammad Azam, Muchammad Mustafid Mustafid Novettralita, Ucky Pradestha Oky Dwi Nurhayati Oky Dwi Nurhayati Otong Saeful Bachri R. Rizal Isnanto R. Rizal Isnanto Rachmat Gernowo Rahmatika, Yuni Ramanda, Febri Retno Hapsari, Angga Ayu Rizky Ayomi Syifa Romadhon, Zainur Royani, Noorhanida Saitem Saitem Salsabila Naqiyah Sri Mulyani Tri Retnaningsih Soeprobowati Tutur Urip Wahyu Adi Wibowo Zainal Bachrudin

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