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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics EMITTER International Journal of Engineering Technology KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Bina Insani ICT Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control INOVTEK Polbeng - Seri Informatika Technomedia Journal Applied Technology and Computing Science Journal Building of Informatics, Technology and Science Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) GUYUB: Journal of Community Engagement Informatics, Electrical and Electronics Engineering Journal Of Artificial Intelligence And Software Engineering Jurnal Informatika: Jurnal Pengembangan IT El-Mujtama: Jurnal Pengabdian Masyarakat The Indonesian Journal of Computer Science
iwan Syarif
Politeknik Elektronika Negeri Surabaya
Religion Aerospace Engineering Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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Semantic Information Search with Automatic Ontology Creation in Regulations National Standards for Higher Education in Indonesia Hidayah, Nadila Wirdatul; Ali Ridho Barakbah; Iwan Syarif
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.v12i3.3207

Abstract

In Indonesia, there are around ten types of legal products that contain higher education regulations. With a large number of articles, more effort is needed when users search for links between one article and another. Based on these problems, it is necessary to have an automatic article representation search system using an automatic ontology. Ontology refers to the hierarchical structure of entities and their relationships. In this paper, the results of the development of an information retrieval system with an automated ontology will be explained. This system describes a process begins with receiving input of higher education regulatory files which are used as data samples Permendikbud No 3 of 2020. Then split the data into articles, paragraphs and contents which are then formed ontologies by building 3 detection functions (Definitive Creation, Compound Creation, and Reference Detection). System output has an accuracy of search results reaching an accuracy of 92.5%.
Klasifikasi Tumor Otak Menggunakan Convolutional Neural Network Fakhri, Haidar; Setiawardhana, Setiawardhana; Syarif, Iwan; Sigit, Riyanto
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.3908

Abstract

Metode klasifikasi citra MRI otak yang digunakan pada penelitian ini adalah Deep Learning dengan Convolutional Neural Network (CNN) dengan 2 model skema arsitektur CNN. Model skema 1 terdapat 2 max pooling layer dan 2 hidden layer, sedangkan model skema 2 terdapat 3 max pooling layer dan 4 hidden layer.  Dataset yang digunakan memuat citra MRI otak manusia dengan total 7023 citra, dengan rincian 1621 Glioma, 1645 Meningioma, 1757 Pituitary, dan 2000 Notumor. Evaluasi F1-Score model skema 1 dan skema 2 berturut-turut: 96% dan 97%, Sedangkan untuk nilai Accuracy yaitu 98%. Hal ini menunjukkan bahwa nilai F1-Score dan Accuracy, model skema 2 lebih baik. Untuk menguji dataset digunakan 10 fold cross-validation menghasilkan nilai rata-rata Accuracy, F1-Score, Precision, dan Recall berturut-turut 0,8520, 0,8470, 0,8493 dan 0,8504, dengan standar deviasi yang kecil, yaitu berturut-turut 0,0352; 0,0346; 0,0337 dan 0,0353 yang menunjukkan bahwa penyimpangan sebaran nilai semakin mendekati nilai rata-ratanya. nilai metrik F1-score dan accuracy berturut-turut, 97,47% dan 97,39%. Hasil accuracy penelitian ini lebih tinggi dibandingkan dengan beberapa penelitian sebelumnya, yakni dari [1], [2], [3], [5], [7], dan [8], berturut-turut: 94.39%, 97.54%, 97.18%, 96.08%, 96,36%, dan 95.55%.
Development of a Mobile Application for Plant Disease Detection using Parameter Optimization Method in Convolutional Neural Networks Algorithm Alwan Fauzi; Iwan Syarif; Tessy Badriyah
EMITTER International Journal of Engineering Technology Vol 11 No 2 (2023)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v11i2.808

Abstract

Plant diseases are a serious problem in agriculture that affects both the quantity and quality of the harvest. To address this issue, authors developed a mobile software capable of detecting diseases in plants by analyzing their leaves using a smartphone camera. This research used the Convolutional Neural Networks (CNN) method for this purpose. In the initial experiments, authors compared the performance of four deep learning architectures: VGG-19, Xception, ResNet-50, and InceptionV3. Based on the results of the experiments, authors decided to use the CNN Xception as it yielded good performance. However, the CNN algorithm does not attain its maximum potential when using default parameters. Hence, authors goal is to enhance its performance by implementing parameter optimization using the grid search algorithm to determine the optimal combination of learning rate and epoch values. The experimental results demonstrated that the implementation of parameter optimization in CNN significantly improved accuracy in potato plants from 96.3% to 97.9% and in maize plants from 87.6% to 93.4%.
Autism Detection based on Deep Learning Walujo, Ivana Yudith; Iwan Syarif; Arna Fariza
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): 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.v13i6.4552

Abstract

Autism Spectrum Disorder (ASD) is a complex developmental condition that affects communication and behavior, with prevalence rates increasing significantly in recent years [1]. According to recent research, early detection remains a challenge but is essential for effective intervention. This study leverages deep learning, specifically the ResNet 34 model, to analyze facial features in children, facilitating early detection of ASD. Using cross-validation to ensure robust model performance, the approach achieved an accuracy rate of 87% with ResNet 34 and 86% with cross-validation. This study contributes to the field by offering a non-invasive diagnostic aid that can help healthcare providers recognize ASD traits through facial analysis. The findings highlight the potential of deep learning in advancing ASD detection, with future work aimed at expanding the dataset and improving model precision.
Membangun Sistem Rekomendasi Hotel dengan Content Based Filtering Menggunakan K-Nearest Neighbor dan Haversine Formula Muliawan, Agung; Badriyah, Tessy; Syarif, Iwan
Technomedia Journal Vol 7 No 2 October (2022): TMJ (Technomedia Journal)
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (546.579 KB) | DOI: 10.33050/tmj.v7i2.1893

Abstract

Peningkatakan pertumbuhan industri hotel pada tiap tahunnya dan preferensi konsumen yang bervariasi dalam kebutuhan layanan hotel mengakibatkan konsumen lebih konsumtif dalam memilih hotel. Kurangnya pilihan kriteria bobot pada penyedia layanan hotel mengakibatkan konsumen mengalami kesulitan dalam memilih hotel yang sesuai dengan preferensinya, sehingga diperlukan sebuah sistem rekomendasi hotel sebagai pilihan alternatif dalam memilih hotel. Dalam penelitian ini digunakan permodelan Case Based Reasoning (CBR) untuk memberikan pembelajaran kepada sistem. Pilihan dari user pada pilihan hotel secara otomatis akan disimpan ke dalam database dan dijadikan sebagai data training sehingga sistem akan mendapatkan informasi secara berkelanjutan. Pada penelitian ini diberikan tiga jenis kebutuhan antara lain Kebutuhan Prioritas (KP), Kebutuhan Umum (KU) dan Kebutuhan Tambahan (KT) dan atribut yang digunakan terdapat enam yaitu: fasilitas, lokasi, harga, tipe kamar, bintang dan skor yang sangat mempegaruhi hasil rekomendasi. Untuk setiap nilai bobot yang ada, dilakukan uji validitas bobot kepentingan menggunakan pairwise comparison matrix (PCM) sehingga nilai bobot menjadi valid dengan rentang nilai 0-1. Selain itu penerapan content based filtering menggunakan metode haversine formula dan K-Nearest Neighbor (KNN) dalam menentukan nilai terdekat dengan data training. Dari eksperimen, didapatkan hasil pengukuran performansi yang memuaskan berupa rata-rata kemiripan (similarity) sebesar 84.50% Kata kunci  : Case Based Reasoning, Content Based Filtering, Haversine Formula, K-Nearest
Evaluation of Stratified K-Fold Cross Validation for Predicting Bug Severity in Game Review Classification Mayangsari, Mustika Kurnia; Syarif, Iwan; Barakbah, Aliridho
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 3, August 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i3.1740

Abstract

Steam review data provides a lot of information for the game development team, either positive or negative reviews. It is essential as negative and positive reviews provide crucial information, and 7% of positive reviews contains bug reports. These bug reports were captured after the game was released, and many reports of common problems still exist. If players found an issue in the game, they could report it directly through the review feature provided by the online game platform. However, it took a long time for the development team to manually analyze and classify the reviews. This study proposed a new approach to automatically classify the reviews on Steam based on the bug severity level. Therefore, to solve this problem, we recommend a solution based on the research background indicated above. For this experiment, we analyzed reviews on two popular game titles namely, FIFA 23 and Apex Legends. We implemented three different classifiers, namely KNN, Decision Tree, and Naïve Bayes, which would be used to train a dataset to classify the bug severity level. Due to the imbalanced dataset, we performed cross-validation to reduce bias in the dataset.  Performance in this model would be evaluated using accuracy rate, precision, recall, and F1 score. As a result, the experiment showed that game reviews of different game titles achieved different accuracy scores. The game review classification for FIFA 23 performed better than the game review classification for Apex Legends. The mean accuracy score of FIFA 23 was 72% with Decision Tree and Apex Legend was 64% with KNN.
Tuberculosis Detection based on Lung X-Ray Images Using Convolutional Neural Networks (CNN) Kurniawan, Rudi; Badriyah, Tessy; Syarif, Iwan
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 7 No 1 (2024): June
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v7i1.6448

Abstract

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis, primarily affecting the lungs. Despite being preventable and curable, TB remains a significant global health issue, especially in developing countries. The success of TB treatment heavily depends on the accuracy of the diagnosis, which typically requires expertise from pulmonology or radiology specialists to interpret chest X-ray images.  This study aims to design an assistive tool for TB detection that can automatically diagnose the disease using chest X-ray data.  The study implemented a Convolutional Neural Network (CNN) architecture to analyze the X-ray images. Additionally, image preprocessing and early stopping methods were employed to enhance accuracy performance, optimize computation, and prevent overfitting.  Experiment was conducting using 75% of the data as training data to generate the model and then applied to 25% of the data as testing data. This study comparing image sizes in RGB and grayscale modes. Experimental results show that the use of early stopping has a significant impact on training time, reducing training time substantially in almost all scenarios without drastically sacrificing accuracy. Without early stopping, accuracy does tend to be higher, as seen in grayscale color mode with an image size of 128x128, where the accuracy reaches 0.992, and in RGB mode with an image size of 64x64 which reaches 0.995. However, training time also increases significantly, for example for a 299x299 image with RGB mode, the training time reaches 927 seconds. Therefore, while RGB yields slightly higher accuracy, grayscale is recommended due to significantly faster training times. Additionally, the early stopping mechanism proves effective in reducing computational time, making the training process more efficient.
Incremental News Mining Using Evolving Clustering with Functional Operators Hidayah, Amalia Wirdatul; Barakbah, Ali Ridho; Syarif, Iwan
The Indonesian Journal of Computer Science Vol. 12 No. 2 (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.v12i2.3197

Abstract

Online media publish journalistic products, one of which is news online (online news). This is in line with the findings of the Ministry of Communication and Informatics (Kemkominfo), that in 2018 there were 43,000 online media in Indonesia. On generally in getting actual news, humans tend to read the news on online media one by one. The activity is not effective because of the news that produced by online media have the same information with each other news. In this study, we propose an innovative solution to this issue by developing a news mining system that employs clustering based on an evolving system. This system has the potential to improve the effectiveness of news retrieval by grouping similar news together and identifying key information trends, ultimately enhancing the ability of individuals to obtain actual news. Based on research observations, the performance of news clustering using an evolving clustering system with functional operators is quite good, as evidenced by an accuracy of 83%.
METODE STATISTIK DAN MACHINE LEARNING UNTUK PREDIKSI HARGA BAHAN POKOK DI JAWA TIMUR Dzulfiqar, Achmad Fakhri; Ferry Astika Saputra; Iwan Syarif
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): 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.v14i1.4625

Abstract

Price fluctuations of basic commodities impact economic stability and community welfare. This study compares predictive methods based on statistical approaches (Simple Moving Average, Linear Regression) and machine learning techniques (Support Vector Regression, Long Short-Term Memory) using data from SISKAPERBAPO, which records daily prices of 76 basic commodities across 119 central markets in 38 districts/cities in East Java. The study supports the role of Regional Inflation Control Teams (TPID) in maintaining stable and low inflation through coordinated policies. Evaluation based on Root Mean Square Error (RMSE) and Squared Correlation indicates that SVR performs best of 4 commodities (rice, sugar, chicken meat, chicken eggs), while LSTM excels for 3 commodities (cooking oil, beef, garlic). These findings recommend SVR and LSTM as the most efffective methods for price prediction and provide a reference for TPID and policymakers in developing for price control.
Implementation of Cyber Threat Intelligence on Intrusion Detection System using STIX Framework Mahardhika, Yesta Medya; Saputra, Ferry Astika; Syarif, Iwan; Wibowo, Prasetyo; Ardhani, Misbahul
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6518

Abstract

Cyber threats are complex and diverse issues. Various types of threats emerge daily on the internet. In this research, we proposed a new Cyber Threat Intelligence platform to deal with the challenges above, using Snort as a tool for detecting anonymous network traffic and STIX as a serialization format and standardization of Cyber Threat Intelligence data. As a result, a Cyber Threat Intelligence based on Snort contains Apache Spark as the processing engine, MongoDB as the database, and STIX as the serialization format and data standardization. We test our platform by using two data sources, the CIC-IDS2017 dataset, and the real traffic. We successfully converted the snort alerts to STIX format and visualized them into graph. The graph shows indication of network traffic suspicious, the country of attacker come from, attribute information and attack pattern. The experiment shows that converting Snort data to STIX requires considerable time if the amount of data processed is getting bigger, Real Traffic needs 16 seconds of data preprocessing and 3 minutes of conversion time, while PCAP needs 35 seconds of preprocessing time and 13 minutes of conversion time.
Co-Authors Achmad Thorikul Huda Adam Ghazy Al Falah Adam Prugel-Bennett Agung Muliawan Ahmad Syauqi Ahsan Ahsan, Ahmad Syauqi Aidil Saputra Kirsan Aidil Saputra Kirsan Aji , Rendra Suprobo Al Falah, Adam Ghazy Alfaqih, Wildan Maulana Akbar Ali Ridho Barakbah Alwan Fauzi Amalia Wirdatul Hidayah Andhik Ampuh Yunanto APRIANDY, KEVIN ILHAM Ardhani, Misbahul Arna Fariza Assodiky, Hilmy Assodiky, Hilmy Asy Syaffa Khoirunnisa Ayu Ahadi Ningrum Aziz, Adam Shidqul Bagas Dewangkara Bima Sena Bayu Dewantara Binti Kholifah Dadet Pramadihanto Daisy Rahmania Syarif Darmawan, Zakha Maisat Eka Desy Intan Permatasari Desy Intan Permatasari, Desy Intan Deyana Kusuma Wardani Dian Neipa Purnamasari Dimas Bagus Santoso Dona Wahyudi Dzulfiqar, Achmad Fakhri Edelani, Renovita Edi Satriyanto Entin Martiana Kusumaningtyas Fahrudin, Tresna Maulana Fahrudin, Tresna Maulana Fakhri, Haidar Fathoni, Kholid Fauzy, Aryazaky Iman Ferry Astika Saputra Ferry Astika Saputra Ferry Astika Saputra Fitri Setyorini Gary Wills Grezio Arifiyan Primajaya Gunawan, Agus Indra Hardiyanti, Fitriani Rohmah Hasan Basri Hidayah, Amalia Wirdatul Hidayah, Nadila Wirdatul Hisyam, Masfu Hisyam, Masfu Idris Winarno Izza Nur Afifah Khoirunnisa, Asy Syaffa Kholifah, Binti Kindarya, Fabyan Kusuma, Harun Indra Kusuma, Selvia Ferdiana M Udin Harun Al Rasyid, M Udin Harun Mahardhika, Yesta Medya Maulana, Yufri Isnaini Rochmat Maulana, Yufri Isnaini Rochmat Mayangsari, Mustika Kurnia Mufid, Mohammad Robihul Mufid, Mohammad Robihul Muhammad Fajrul Falah Muhlis Tahir Nadila Wirdatul Hidayah Nana Ramadijanti Nana Ramadijanti, Nana Novie Ayub Windarko Nur Rosyid Mubtadai Nur Rosyid Mubtadai, Nur Rosyid Nur Sakinah Nur Ulima Rusmayani Oktavia Citra Resmi Rachmawati Osamah Abdullah Yahya Amran Prasetyo Primajaya, Grezio Arifiyan Puspasari Susanti Rabiatul Adawiyah Reesa Akbar Rengga Asmara Rengga Asmara Renovita Edelani Riyanto Sigit, Riyanto Rizky Yuniar Hakkun Rosmaliati, Rosmaliati Rozie, Fachrul Rudi Kurniawan Rudi Kurniawan Rulisiana Widodo S, Ferry Astika S, Ferry Astika Sa'adah, Umi Selvia Ferdiana Kusuma Setiawardhana Setiawardhana Setiawardhana, Setiawardhana Shabirin, Irsal Silfiana Nur Hamida Sritrusta Sukaridhoto Sudaryanto, Aris Sumarsono, Irwan Tahir, Muhlis Tessy Badriyah, Tessy Tri Harsono Ubed, Imanullah Ali Umi Sa'adah Utomo, Agus Priyo Wahjoe Tjatur Sesulihatien Walujo, Ivana Yudith Wibowo, Prasetyo Willy Sandhika