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All Journal Jurnal Teknik ITS JURNAL REKAYASA TEKNOLOGI INFORMASI Voice Of Informatics CSRID (Computer Science Research and Its Development Journal) Jurnal Review Pendidikan dan Pengajaran (JRPP) Jurnal Mantik Journal of Intelligent Decision Support System (IDSS) Nusantara Hasana Journal Teknik: Jurnal Ilmu Teknik dan Informatika Jurnal Abdi Masyarakat Indonesia Jurnal Mandiri IT Brilliance: Research of Artificial Intelligence Jurnal Minfo Polgan (JMP) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Informatika dan Teknologi Informasi JSITP Jurnal Indonesia Sosial Teknologi Journal of Artificial Intelligence and Digital Business Jurnal Masyarakat Madani Indonesia Jurnal Ekonomi, Teknologi dan Bisnis Innovative: Journal Of Social Science Research SOROT: Jurnal Pengabdian Kepada Masyarakat SmartComp Jurnal Akademik Pengabdian Masyarakat Svarga Pena: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika dan Desain Komunikasi Visual
Sarif Surorejo
STMIK YMI Tegal
Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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Klasifikasi Jenis Buah Apel Menggunakan Algoritma Convolutional Neural Network Fadlilah, Chairil Aditya Nur; Surorejo, Sarif; Arif , Zaenul
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 3 (2025): Agustus - October
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i3.2759

Abstract

Penelitian ini bertujuan untuk membantu mempercepat klasifikasi buah apel berdasarkan jenis. Data yang digunakan sebanyak 2800 gambar buah apel berasal dari Kaggle yang dibagi menjadi 4 kategori jenis, yaitu apel Golden, Granny Smith, Red Delicious, Red Yellow. Metode yang digunakan untuk mengumpulkan data yaitu dengan metode penelitian kualitatif. Klasifikasi dibuat menggunakan model machine learning dengan metode Convolutional Neural Network (CNN) dan evaluasi hasilnya menggunakan confusion matrix. Dilakukan 2 kali percobaan dengan 20 dan 50 epoch untuk dapat mengetahui hasil yang optimal. Hasil dari penelitian menunjukan bahwa gambar dapat diprediksi dengan benar dengan tingkat akurasi yang tinggi mencapai 100%. Hasil model yang telah ditraining selanjutnya digunakan untuk prediksi gambar yang dapat diakses melalui website. Pengklasifikasian pada website dibuat menggunakan library flask dan hasil prediksinya tersimpan kedalam file dengan format hdf5.
Digital Marketing Efforts to Improve Products of Micro Small and Medium Enterprises (UMKM) in Tegal Santoso, Nugroho Adhi; Nugroho, Bangkit Indarmawan; Murtopo, Aang Alim; Surorejo, Sarif; Gunawan, Gunawan
Brilliance: Research of Artificial Intelligence Vol. 3 No. 2 (2023): Brilliance: Research of Artificial Intelligence, Article Research November 2023
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v3i2.3646

Abstract

Digital marketing is promotional activities and market search through the media digitally online by utilizing various means such as social networks. The aim of this research is to increase knowledge and skills about digital marketing, especially social media, for Small and Medium Enterprises (SME) business people to increase their sales and profits. Digital marketing is the use of social media networks to carry out promotional activities and map digital markets. By using computers or other electronic equipment, digital marketing ideas can bring together geographically diverse parties. The aim of this research is to identify the most effective digital marketing tactics for the growth of MSMEs in Tegal City and Tegal Regency. The method used in this research is descriptive qualitative. With Data collection through observation, interviews, and secondary sources, such as books, journals, and articles, were used to collect information for this research. The results of this research show that the productivity growth of MSMEs in Tegal City and Tegal Regency has not been positive. Even when a website for an online business has been created, not everyone has implemented a digital marketing plan. It can be seen that digital marketing strategies have not received much attention from MSMEs in Tegal City and Tegal Regency. So it is hoped that MSMEs in Tegal City and Tegal Regency can adapt to changing times, namely selling online using digital marketing strategies.
Application of computer vision techniques to detect diseases and pests of chili plants Nurokhman, Akhmad; Surorejo, Sarif; Kurniawan, Rifki Dwi; Gunawan, Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i1.201

Abstract

This research aims to develop a disease and pest detection system in chili plants using computer vision techniques. In this study, deep learning methods, especially Convolutional Neural Networks (CNN), were applied to identify and classify various types of diseases and pests that often attack chili plants. The data used included images of chili leaves infected with various diseases and pests, which were then trained in CNN models to recognize certain patterns that indicate the presence of infection. The results showed that the developed system was able to detect and classify diseases and pests in chili plants with a very high degree of accuracy. The novelty of this research lies in the use of computer vision techniques combined with sophisticated deep learning algorithms to automatically detect diseases and pests, which were previously done manually by farmers or agricultural experts. These findings make an important contribution to improving efficiency and effectiveness in chili crop health management, offering innovative solutions to support agricultural sustainability through the use of advanced technology.
Application of fuzzy expert system method for early detection of dengue fever Prayoga, Alan Eka; Surorejo, Sarif; Kurniawan, Rifki Dwi; Gunawan, Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i1.217

Abstract

The application of the Fuzzy Expert System method in the early detection of dengue fever offers a promising approach to improve diagnostic accuracy. This study aims to develop a system that can overcome the diversity of dengue fever symptoms and uncertainty in the diagnosis process. Using medical record data of patients who have confirmed DHF, the study designed fuzzy rules for symptom evaluation, resulting in more precise diagnostic outputs. The results indicate the system's success in identifying dengue cases with high sensitivity and good positive predictive value. These findings confirm the importance of FES technology in clinical practice, especially for controlling and preventing dengue fever in endemic areas. Continued research will test this system in a broader clinical scenario to ensure its effectiveness and practicality in diverse medical environments.
Implementation of fuzzy mamdani method in predicting cayenne chili prices in Tegal Regency Surorejo, Sarif; Mutaqin, Ahadan Fauzan; Kurniawan, Rifki Dwi; Gunawan, Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 2 (2024): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i2.231

Abstract

This study investigates the application of Fuzzy Mamdani's method in predicting the price of cayenne pepper in Tegal Regency, one of the important agricultural commodities that has significant economic implications. This study aims to develop an accurate and reliable cayenne pepper price prediction model in Tegal Regency using the fuzzy Mamdani method. Research methods include collecting historical data on cayenne pepper prices, cayenne pepper production, and rainfall, as well as the implementation of the Mamdani fuzzy method consisting of fuzzification, inference, and defuzzification using Python programming language computing. The results showed that the fuzzy Mamdani method can predict the price of cayenne pepper with a good level of accuracy, with an average prediction error of 16.653285% and a prediction correctness rate of 83.346715%. This finding has implications for improving production planning capabilities and marketing strategies for cayenne pepper farmers in Tegal District, as well as contributing to the scientific literature in the application of fuzzy methods in agriculture
Application of artificial neural network method for early detection of dengue fever Surorejo, Sarif; Ningrum, Isna Lidia; Ananda, Pingky Septiana; Gunawan, Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 2 (2024): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i2.240

Abstract

Dengue fever is a tropical disease whose diagnosis is often delayed due to limitations of conventional diagnostic methodologies, which have an impact on the effectiveness of medical interventions. This research is designed to develop an Artificial Neural Network (ANN) model aimed at improving accuracy and speed in dengue diagnosis. Through quantitative methods, clinical data from 50 patients during the period 2020-2021 were analyzed using machine learning techniques to train the ANN model, including the process of data normalization and selection of relevant features. The test results of the model showed excellent diagnostic performance with accuracy reaching 87%, precision 92%, and F1-Score 92%, indicating its effective ability to identify positive and negative cases. The conclusion of this study is that the developed ANN model is able to overcome the limitations of conventional diagnostics and shows significant potential in improving medical responses to dengue outbreaks. Further research is recommended to expand the datasets used in order to improve the validation and generalization of the model in the context of broader clinical applications
Detection of normal chicken meat and tiren chicken using naïve bayes classifier and glcm feature extraction Surorejo, Sarif; Ubaidillah, Muhamad Rizal; Syefudin, Syefudin; Gunawan, Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 2 (2024): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i2.245

Abstract

The chicken farming industry is an important sector in the Indonesian economy, but there are food security issues with the presence of tiren chicken. This research aims to develop a more accurate and efficient method of detection of tiren chickens using Naive Bayes Classifier with Gaussian and Bernoulli kernels and GLCM feature extraction. Data is collected from various internet sources, then pre-processing and feature extraction is carried out. The Naive Bayes Classifier algorithm is implemented with two kernels and evaluated using accuracy, precision, recall, and f1-score metrics. The Gaussian kernel showed an accuracy of 0.75, higher than Bernoulli's kernel which was only 0.50. Models with Gaussian kernels have high performance in detecting tiren chickens and normal chicken precision. The combination of Gaussian and Bernoulli kernels and GLCM feature extraction is effective in improving the detection accuracy of tiren chickens, contributing significantly to food safety and consumer confidence
Penerapan Metode Dobel Exponential dan Smoothing Analytical Hierarchy Process untuk Prediksi Tingkat Kerawanan Tanah Longsor Di Kabupaten Brebes Putra, Alif Sya’Bani; Surorejo, Sarif; Andriani, Wresti; Gunawan, Gunawan
Innovative: Journal Of Social Science Research Vol. 4 No. 3 (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.v4i3.10505

Abstract

Pengembangan metode prediksi tingkat kerawanan tanah longsor di Kabupaten Brebes menggunakan kombinasi double exponential smoothing dan analytical hierarchy process (AHP). Tujuan penelitian ini adalah meningkatkan pemahaman dan prediksi terhadap fenomena tanah longsor dan memanfaatkan data historis dan analisis kriteria multi-faktor. Metodologi penelitian ini melibatkan analisis seri waktu menggunakan double exponential smoothing untuk memprediksi variabel-variabel penting seperti curah hujan, dan pergerakan tanah. Sementara AHP digunakan untuk menilai dan mengintegrasikan berbagai faktor risiko tanah longsor, termasuk kondisi geologi, kemiringan lereng, dan penggunaan lahan. Hasil penelitian ini adalah model yang diusulkan mampu memprediksi tingkat kerawanan tanah longsor dengan akurasi yang lebih tinggi dibandingkan metode yang ada. Penelitian ini memberikan kontribusi penting dalam upaya mitigasi bencana tanah longsor di Kabupaten Brebes, serta membuka peluang untuk aplikasi metode serupa di wilayah lain yang memiliki risiko tanah longsor.
Application of association rule for prediction of menu ordered at café minapadi Zain Hidayatullah, Fikri; Surorejo, Sarif; Andriani, Wresty; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 12 No. 4 (2024): April: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v12i4.279

Abstract

This research aims to develop a predictive model that helps prepare menus based on customer preferences at Café Minapadi, hoping to improve operational efficiency and customer satisfaction. Using rule-association data mining techniques, the study uncovered hidden patterns in extensive transaction data, applying a priori algorithms in datasets to explore menu ordering frequencies and trends. Data analysis includes cleansing, transforming, and selecting features to generate relevant insights. The results found that items such as coffee and chocolate cake were often purchased together, providing an opportunity for menu optimization and special promotions. Evaluation of predictive models shows the possibility of increased accuracy in stock preparation and adjustment of menu offerings, providing significant benefits in business decision-making in the culinary sector.
Prediction of Bank Central Asia stock prices after dividend distribution using ARIMA method Surorejo, Sarif; Sulthon, Muhammad; Anandianskha, Sawaviyya; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.294

Abstract

This study explores the prediction of Bank Central Asia (BBCA) stock prices following the annual dividend distribution using the Autoregressive Integrated Moving Average (ARIMA) method. The primary goal is to provide a robust forecasting tool to aid investors and financial analysts in making informed decisions. The research employs a quantitative approach with a quasi-experimental design, analyzing weekly BBCA stock price data from January 2019 to February 2024. The findings demonstrate that the ARIMA (2, 1, 2) model provides stable and reliable predictions of BBCA stock prices, showing slight weekly variations but overall stability. Practically, these predictive models can be integrated into a web-based platform, allowing real-time stock price forecasting and broader accessibility for users. This study contributes to the financial literature by validating the ARIMA model's applicability in the Indonesian stock market and suggesting the exploration of hybrid models and external economic factors for future research.
Co-Authors ., Yustira Aang Alim Murtopo Adhi Santoso, Nugroho Al Fattah, Muhammad Raikhan Albana, Muhammad Syifa Ali Djamhuri Alzam Habibie Ananda, Pingky Septiana Anandianskha, Sawaviyya Andriani, Wresti Andriani, Wresty Arif , Zaenul Arif, Zaenul Aslam, Muhammad Nur Bangkit Indarmawan Nugroho Bayu Aji Santoso Cahyo, Septian Dwi Defi Lugianti Divia Faiqotul Cahyati Dwi Kurniawan, Rifki Erni Unggul Sedya Utami, Erni Unggul Sedya Fadlilah, Chairil Aditya Nur Firmansyah, Muchamad Aries Gunawan Gunawan Gunawan Gunawan Gunawan Hastin Setyorini Isnaeni Hamidah Juniyanto, Rudi Karsidin, Karsidin Khofifah Indah Hasanah Khofifah Indah Hasanah Kurniawan, Rifki Dwi Maulana, M Taufik Fajar Milkhatunisya Milkhatunisya Milkhatunisya, Milkhatunisya Moh. Jamaludin Mohamad Rifki Septiadi Muhamad Lutfi Muhammad Alfan Maulana Muhammad Sulthon Muhammad Syahrul Maulana Muhammad Syahrul Maulana Mutaqin, Ahadan Fauzan Ningrum, Isna Lidia Nugroho Adhi Santoso Nugroho Adhi Santoso Nur Kholifatul Aula Nurokhman, Akhmad Nurul Fadilah, Nurul Pingky Septiana Ananda Pinky Septiana Prayoga, Alan Eka Putra, Alif Sya’Bani Rifki Dwi Kurniawan Rifki Dwi Kurniawan Rito Cipta sigitta Hariyono Rivaldiansyah, Rafik Romadhona, Wahyu Rudi Juniyanto Sagita, Rito Cipta Santoso, Aisyach Aminarti Santoso, Bayu Aji Santoso, Nugroho Adh Santoso, Nugroho Adhi Setiawati, Windi Subechi, Fadlan Hafid Syefudin Syefudin Syefudin Syefudin, Syefudin Ubaidillah, Muhamad Rizal umar, moh azizul umar Uswatun Khasanah Wresti Andriani Yustira . Zaenal Arif Zaenul Arif Zain Hidayatullah, Fikri