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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Teknologi dan Manajemen Informatika Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Technology and Computer Science (JOINTECS) J I M P - Jurnal Informatika Merdeka Pasuruan Conference on Innovation and Application of Science and Technology (CIASTECH) JAST : Jurnal Aplikasi Sains dan Teknologi Jurnal Komputer, Informasi dan Teknologi Prosidia Widya Saintek Journal of Computing Theories and Applications Journal of Future Artificial Intelligence and Technologies
Syahroni Wahyu Iriananda, Syahroni Wahyu
Program Studi S1 Teknik Informatika, Fakultas Teknik, Universitas Widyagama Malang
Agriculture, Biological Sciences & Forestry Automotive Engineering 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 Social Sciences Transportation Other

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APLIKASI SISTEM PAKAR DIAGNOSA PENYAKIT PADA BATITA BERBASIS WEB DENGAN METODE CERTAINTY FACTOR Yudid Nuriantono; Fitri Marisa; Rangga Pahlevi Putra; Syahroni Wahyu Iriananda; Anik Vega Vitianingsih
Prosidia Widya Saintek Vol 1, No 1 (2022)
Publisher : Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (372.23 KB)

Abstract

Dalam kondisi saat ini usia – usia  yang sangat rentan terserang penyakit mulai usia 0 - 36 bulan atau usia Batita, karena disebabkan oleh sistem imun di dalam tubuh anak yang memang belum terbangun dengan sempurna atau sedang dalam kondisi yang lemah. Sering kali para orang tua tidak tahu apa yang harus dilakukan kepada anak-anak mereka saat anak sedang sakit atau salah mengartikan gejala yang terjadi pada anak tersebut. Oleh sebab itu, di perlukan system yang dapat membantu orang tua, terutama orang tua muda atau baru mempunyai buah hati dan minim akan pengetahuan akan batita, dalam penelitian ini  menggunakan   metode Certainty Factor untuk mendiagnosa penyakit pada batita. Berdasarkan 22 data yang telah di uji menggunakan precission and recall sebesar 86,36% untuk nilai ketepatan (precission) dan sebesar 100% untuk nilai keberhasilan (recall). Dari nilai precission and recall system dapat berfungsi meskipun masih jauh dari 100%, tapi masih dapat digunakan untuk user mendiagnosa penyakit pada batita. Dengan adanya system tersebut diharapkan dapat membantu para orangtua untuk menentukan penyakit yang diderita dengan cepat.  
Implementasi Sinici Kudo Apps pada Peternakan Kelinci Peci P’Rama di Kabupaten Tulungagung Fadhillah, Arief Rizki; Iriananda, Syahroni Wahyu; Purnomowati, Wiwin; Nugroho, Kuncahyo Setyo; Akbar, Ismail; Sakinah, Renada Julia
JAST : Jurnal Aplikasi Sains dan Teknologi Vol 6, No 1 (2022): EDISI JUNI 2022
Publisher : Universitas Tribhuwana Tunggadewi Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33366/jast.v6i1.3144

Abstract

Broiler rabbits have a very high potential to improve the community's economy. In addition, rabbits can also be a potential source of animal protein as an alternative food for the community. Peci P'Rama (Home Broiler Rabbit Breeder) is a broiler rabbit farming business initiated in October 2017. The problems of Peci P'Rama in running a broiler rabbit farm, among others: have not carried out the codification/identification of broiler rabbits owned according to standards, and the financial management carried out is not well organized and structured. In solving the problems faced by Peci P'Rama partners, the implementing team designs activity methods for problem-solving: Planning, Analysis, Problem Identification, Design, Implementation, Testing and Integration, Training and Mentoring, Maintenance, Evaluation of the Use of Indonesia's Leading Rabbit Farm Information System (SiNiCi Kudo) Mobile Based. Based on the results of the Sinici Kudo application on the Peci P'Rama rabbit farm, it can be concluded that with the implementation of the Sinici Kudo application on the P'Rama rabbit farm, an increase in rabbit productivity and financial management went well and was recorded neatly.ABSTRAKTernak kelinci pedaging memiliki potensi yang sangat tinggi dalam meningkatkan perekonomian masyarakat. Selain itu, ternak kelinci juga dapat sebagai potensi sumber protein hewani alternatif pengan bagi masyarakat. Peci P’Rama (Peternak Kelinci Pedaging Rumahan) merupakan usaha ternak kelinci pedaging yang dirintis sejak Bulan Oktober Tahun 2017. Permasalahan Peci P’Rama dalam menjalankan peternakan kelinci pedaging, antara lain : belum melakukan kodifikasi/pemberian identitas pada kelinci pedaging yang dimiliki sesuai standar, pengelolaan keuangan yang dilakukan belum tertata dan terstruktur baik. Dalam menyelesaikan permasalahan yang dihadapi mitra Peci P’Rama, maka tim pelaksana merancang metode kegiatan untuk penyelesaian permasalahan : Planning, Analisis dan Identifikasi Masalah, Desain, Implementasi, Ujicoba dan Integrasi,  Pelatihan dan Pendampingan, Maintanance, Evaluasi Penggunaan Sistem Informasi Peternakan Kelinci Unggulan Indonesia (SiNiCi Kudo) Berbasis Mobile. Berdasarkan hasil implementasi aplikasi sinici kudo pada peternakan kelinci Peci P’Rama, maka dapat disimpulkan bahwa dengan implementasi Aplikasi Sinici Kudo pada peternakan kelinci P’Rama didapatkan peningkatan produktifitas ternak kelinci dan pengelolaan keuangan berjalan dengan baik dan tercatat dengan rapi.Kata Kunci : ternak kelinci,  Sinici kudo, kodefikasi, Keuangan
Optimasi Klasifikasi Sentimen Komentar Pengguna Game Bergerak Menggunakan Svm, Grid Search Dan Kombinasi N-Gram Iriananda, Syahroni Wahyu; Budiawan, Renaldi Widi; Rahman, Aviv Yuniar; Istiadi, Istiadi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 4: Agustus 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.1148244

Abstract

Game online telah menjadi fenomena budaya signifikan dalam industri yang berkembang pesat. Pengguna dan pengembang game menggunakan analisis sentimen untuk memahami opini dan ulasan pemain, yang membantu dalam pengembangan dan peningkatan game. Penelitian ini melakukan klasifikasi sentimen menggunakan algoritma Support Vector Machine (SVM) dengan penerapan teknik N-Gram untuk seleksi fitur. Grid Search (GS) digunakan untuk optimasi hyperparameter guna mencapai akurasi optimal. Eksperimen dilakukan dengan berbagai skenario, termasuk variasi jumlah data, pengaturan hyperparameter, rasio dataset pelatihan dan pengujian, serta konfigurasi N-Gram. Kinerja model dinilai menggunakan metrik seperti Akurasi, Presisi, Recall, dan Area di Bawah Kurva ROC (AUC). Hasil menunjukkan bahwa dengan dataset gabungan (Allgame) dan integrasi fitur seleksi N-Gram Unigram, Bigram, dan Trigram (UniBiTri), model ini mencapai akurasi 87,3%, presisi 88,5%, recall 85,5%, dan AUC 0,9081, menggunakan kernel Fungsi Basis Radial (RBF) dengan validasi silang k-fold (k=10).   Abstract   Online gaming has become a significant cultural phenomenon within a rapidly expanding industry. Game users and developers leverage sentiment analysis to understand player opinions and reviews, which subsequently guide game development and enhancements. In this study, sentiment classification was performed using the Support Vector Machine (SVM) algorithm, employing N-Gram techniques for feature selection. Grid Search (GS) was utilized for hyperparameter optimization to achieve the highest possible accuracy. To evaluate the impact of these methods, experiments were conducted across various scenarios, including different data quantities, hyperparameter settings, training and testing dataset ratios, and N-Gram configurations. The performance of the classification model was assessed using metrics such as Accuracy, Precision, Recall, and the Area Under the ROC Curve (AUC). The results of the study indicate that by using 3600 rows from a combined dataset (Allgame) and integrating Unigram, Bigram, and Trigram (UniBiTri) N-Gram selection features, along with k-fold cross-validation (k=10) and the Radial Basis Function (RBF) kernel, the model effectively classifies user reviews. Specifically, the model achieved an accuracy of 87.3%, precision of 88.5%, recall of 85.5%, and an AUC of 0.9081.
Deteksi Objek Video Bahasa Isyarat Untuk Anak Tuna Rungu dan Tuna Wicara Menggunakan YOLOv8 Maheza Fresmanda, Muhammad; Istiadi; Syahroni Wahyu Iriananda
Jurnal Komputer, Informasi dan Teknologi Vol. 4 No. 2 (2024): Desember
Publisher : Penerbit Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/jkomitek.v4i2.1895

Abstract

Penelitian ini bertujuan untuk mengimplementasikan keberhasilan model algoritma YOLOv8 dalam mendeteksi abjad bahasa isyarat Indonesia (BISINDO) berbasis video. BISINDO merupakan adaptasi American Sign Language (ASL) yang disesuaikan dengan budaya Indonesia agar lebih mudah digunakan. Itu bergantung pada gerakan dua tangan untuk komunikasi. Model YOLOv8 mampu mendeteksi dan mengklasifikasikan bahasa isyarat Indonesia dengan tingkat akurasi yang cukup baik, mencapai precision 93,35%, recall 70,25%, mAP50 64,05% dan mAP50-95 45,88%. Temuan ini diharapkan dapat memberikan kontribusi positif dalam pendidikan dan komunikasi untuk anak tunarungu dan tunawicara
Analyzing InceptionV3 and InceptionResNetV2 with Data Augmentation for Rice Leaf Disease Classification Firnando, Fadel Muhamad; Setiadi, De Rosal Ignatius Moses; Muslikh, Ahmad Rofiqul; Iriananda, Syahroni Wahyu
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 1 (2024): June 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-4

Abstract

This research aims to evaluate and compare the performance of several deep learning architectures, especially InceptionV3 and InceptionResNetV2, with other models, such as EfficientNetB3, ResNet50, and VGG19, in classifying rice leaf diseases. In addition, this research also evaluates the impact of using data augmentation on model performance. Three different datasets were used in this experiment, varying the number of images and class distribution. The results show that InceptionV3 and InceptionResNetV2 consistently perform excellently and accurately on most datasets. Data augmentation has varying effects, providing slight advantages on datasets with lower variation. The findings from this research are that the InceptionV3 model is the best model for classifying rice diseases based on leaf images. The InceptionV3 model produces accuracies of 99.53, 58.94, and 90.00 for datasets 1, 2, and 3, respectively. It is also necessary to be wise in carrying out data augmentation by considering the dataset's characteristics to ensure the resulting model can generalize well.
Integrating SMOTE-Tomek and Fusion Learning with XGBoost Meta-Learner for Robust Diabetes Recognition Setiadi, De Rosal Ignatius Moses; Nugroho, Kristiawan; Muslikh, Ahmad Rofiqul; Iriananda, Syahroni Wahyu; Ojugo, Arnold Adimabua
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 1 (2024): June 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-11

Abstract

This research aims to develop a robust diabetes classification method by integrating the Synthetic Minority Over-sampling Technique (SMOTE)-Tomek technique for data balancing and using a machine learning ensemble led by eXtreme Gradient Boosting (XGB) as a meta-learner. We propose an ensemble model that combines deep learning techniques such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU) with XGB classifier as the base learner. The data used included the Pima Indians Diabetes and Iraqi Society Diabetes datasets, which were processed by missing value handling, duplication, normalization, and the application of SMOTE-Tomek to resolve data imbalances. XGB, as a meta-learner, successfully improves the model's predictive ability by reducing bias and variance, resulting in more accurate and robust classification. The proposed ensemble model achieves perfect accuracy, precision, recall, specificity, and F1 score of 100% on all tested datasets. This method shows that combining ensemble learning techniques with a rigorous preprocessing approach can significantly improve diabetes classification performance.
Outlier Detection Using Gaussian Mixture Model Clustering to Optimize XGBoost for Credit Approval Prediction Setiadi, De Rosal Ignatius Moses; Muslikh, Ahmad Rofiqul; Iriananda, Syahroni Wahyu; Warto, Warto; Gondohanindijo, Jutono; Ojugo, Arnold Adimabua
Journal of Computing Theories and Applications Vol. 2 No. 2 (2024): JCTA 2(2) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11638

Abstract

Credit approval prediction is one of the critical challenges in the financial industry, where the accuracy and efficiency of credit decision-making can significantly affect business risk. This study proposes an outlier detection method using the Gaussian Mixture Model (GMM) combined with Extreme Gradient Boosting (XGBoost) to improve prediction accuracy. GMM is used to detect outliers with a probabilistic approach, allowing for finer-grained anomaly identification compared to distance- or density-based methods. Furthermore, the data cleaned through GMM is processed using XGBoost, a decision tree-based boosting algorithm that efficiently handles complex datasets. This study compares the performance of XGBoost with various outlier detection methods, such as LOF, CBLOF, DBSCAN, IF, and K-Means, as well as various other classification algorithms based on machine learning and deep learning. Experimental results show that the combination of GMM and XGBoost provides the best performance with an accuracy of 95.493%, a recall of 91.650%, and an AUC of 95.145%, outperforming other models in the context of credit approval prediction on an imbalanced dataset. The proposed method has been proven to reduce prediction errors and improve the model's reliability in detecting eligible credit applications.
ANALISIS SENTIMEN ULASAN GAME MOBILE FIRST-PERSON SHOOTER DI GOOGLE PLAY STORE MENGGUNAKAN METODE PEMBOBOTAN TF-IDF Iriananda, Syahroni Wahyu; Putra, Rangga Pahlevi; Raihan, Anugrah Ahzul; Saputra, Deni Adi; Verdiansyah, Egi
Prosidia Widya Saintek Vol. 2 No. 2 (2023)
Publisher : Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Paper ini membahas tentang analisis sentimen ulasan game mobile genre FPS menggunakan metode pembobotan TF-IDF. Dalam penelitian ini, penulis menggunakan 2180 ulasan yang telah divalidasi dan dibersihkan, di mana 1258 ulasan diklasifikasikan sebagai positif dan 922 ulasan sebagai negatif. Dengan menggunakan pembobotan TF-IDF dan pengujian model klasifikasi, penelitian ini mencapai tingkat akurasi sebesar 76%, dengan presisi 75%, recall 74%, dan F1-score 75%. Hasil ini menunjukkan bahwa metode pembobotan TF-IDF dapat menghasilkan analisis sentimen yang efektif dan otomatis untuk ulasan game mobile genre FPS, memberikan kontribusi penting dalam pengembangan metode analisis sentimen dalam konteks tersebut.
Co-Authors Akbar, Ismail Akhmad Farid Anastasia Lidya Maukar Anik Yuesti Arie Restu Wardhani Arief Rizki Fadhillah Aviv Yuniar Rahman Aviv Yuniar Rahman Bagus Wisnu Wahyanto Budiawan, Renaldi Widi Dadang Hermawan De Rosal Ignatius Moses Setiadi Dian Kristiawan Nugroho Ferry Irmawan Firman Nurdiyansyah Firnando, Fadel Muhamad Fitri Marisa Fitri Marisa Fitri Marisa Fitri Marisa Harry Soekotjo Dachlan Hesti Sholikah Indra Dharma Wijaya Irham Kurniawan Istiadi Jutono Gondohanindijo, Jutono Leo Hutri Wicaksono Maheza Fresmanda, Muhammad Mardiana Andarwati Mohammad Ridho Prediksi Muhammad Aziz Muslim Muhammad Januar Wicaksono Muslikh, Ahmad Rofiqul Nugroho, Kuncahyo Setyo Ojugo, Arnold Adimabua Purnomowati, Wiwin Putra, Rangga Pahlevi Raihan, Anugrah Ahzul Rangga Pahlevi Putra Rangga Pahlevi Putra Rangga Pahlevi Putra Renaldi Widi Budiawan Sakinah, Renada Julia Saputra, Deni Adi Tubagus Mohammad Akhriza Verdiansyah, Egi Warto - Yudid Nuriantono