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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Masyarakat Informatika Bulletin of Electrical Engineering and Informatics Kreano, Jurnal Matematika Kreatif-Inovatif Sainteknol : Jurnal Sains dan Teknologi International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Abdimasku : Jurnal Pengabdian Masyarakat Unnes Journal of Mathematics Education Unnes Journal of Mathematics Journal of Community Empowerment Proceeding ISETH (International Summit on Science, Technology, and Humanity) Jurnal Inovasi Pengabdian dan Pemberdayaan Masyarakat Recursive Journal of Informatics Kreano, Jurnal Matematika Kreatif Inovatif Jurnal Penelitian Pendidikan Jurnal Dharma Indonesia Jurnal Abdi Negeri Recursive Journal of Informatics
Riza Arifudin
Unknown Affiliation
Humanities Automotive Engineering Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Optimizing Support Vector Machine Performance for Parkinson's Disease Diagnosis Using GridSearchCV and PCA-Based Feature Extraction Jumanto, Jumanto; Rofik, Rofik; Sugiharti, Endang; Alamsyah, Alamsyah; Arifudin, Riza; Prasetiyo, Budi; Muslim, Much Aziz
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.38-50

Abstract

Background: Parkinson's disease (PD) is a critical neurodegenerative disorder affecting the central nervous system and often causing impaired movement and cognitive function in patients. In addition, its diagnosis in the early stages requires a complex and time-consuming process because all existing tests such as electroencephalography or blood examinations lack effectiveness and accuracy. Several studies explored PD prediction using sound, with a specific focus on the development of classification models to enhance accuracy. The majority of these neglected crucial aspects including feature extraction and proper parameter tuning, leading to low accuracy. Objective: This study aims to optimize performance of voice-based PD prediction through feature extraction, with the goal of reducing data dimensions and improving model computational efficiency. Additionally, appropriate parameters will be selected for enhancement of the ability of the model to identify both PD cases and healthy individuals. Methods: The proposed new model applied an OpenML dataset comprising voice recordings from 31 individuals, namely 23 PD patients and 8 healthy participants. The experimental process included the initial use of the SVM algorithm, followed by implementing PCA for feature extraction to enhance machine learning accuracy. Subsequently, data balancing with SMOTE was conducted, and GridSearchCV was used to identify the best parameter combination based on the predicted model characteristics.  Result: Evaluation of the proposed model showed an impressive accuracy of 97.44%, sensitivity of 100%, and specificity of 85.71%. This excellent result was achieved with a limited dataset and a 10-fold cross-validation tuning, rendering the model sensitive to the training data. Conclusion: This study successfully enhanced the prediction model accuracy through the SVM+PCA+GridSearchCV+CV method. However, future investigations should consider an appropriate number of folds for a small dataset, explore alternative cross-validation methods, and expand the dataset to enhance model generalizability.   Keywords: GridSearchCV, Parkinson Disaese, SVM, PCA, SMOTE, Voice/Speech
STEM Trails: Enhancing STEM Education through Math Trails with Digital Technology Cahyono, Adi Nur; Dewi, Nuriana Rachmani; Asih, Tri Sri Noor; Arifudin, Riza; Aditya, Rozak Ilham; Maulana, Bagus Surya; Nugroho, Muhammad Andi
Kreano, Jurnal Matematika Kreatif-Inovatif Vol. 15 No. 1 (2024): Kreano, Jurnal Matematika Kreatif-Inovatif
Publisher : UNNES JOURNAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jtknv202

Abstract

This study aims to explore the development of STEM Trails to improve STEM Education through the integration of Math Trails activity with Digital Technology. An exploratory study initiative engaged researchers, teachers, students, and programmers. Data was collected through discussions and observations and then evaluated to create STEM Education through Math Trails utilizing Digital Technology. The study demonstrated the successful development of STEM Trails, comprising the STEM Trails platform and the creation of STEM Education activities utilizing Math Trails on the platform. STEM Trailblazers create paths with goals focused on science, technology, engineering, and mathematics using elements found in the surroundings. The trail and tasks are posted on a website for STEM Trail walkers to access and investigate via an application. STEM Trails enable the comprehensive exploration of science, technology, engineering, and mathematics through physical or virtual means. The study suggests that STEM Trails can serve as a mathematics learning approach that mixes outdoor activities with digital technologies and incorporates other subjects. Math Trails is a traditional concept that has been innovatively combined with digital technologies and diverse activities. This method must be broadened and refined by adjusting to the specific circumstances, conditions, and requirements, and then executed in different settings. Penelitian ini bertujuan untuk mengeksplorasi pengembangan STEM Trails untuk meningkatkan STEM Education melalui integrasi aktivitas Math Trails dengan Digital Technology. Sebuah inisiatif penelitian eksploratif melibatkan peneliti, guru, siswa, dan programmer. Data dikumpulkan melalui diskusi dan pengamatan dan kemudian dievaluasi untuk menciptakan STEM Education melalui Math Trails menggunakan Teknologi Digital. Studi ini menunjukkan keberhasilan pengembangan STEM Trails, yang terdiri dari platform Stem Trails dan penciptaan kegiatan STEM Education menggunakan Math Trails di platform tersebut. STEM Trailblazers menciptakan jalur dengan tujuan yang berfokus pada ilmu pengetahuan, teknologi, teknik, dan matematika menggunakan elemen yang ditemukan di sekitarnya. Trail dan tugas diposting di situs web untuk STEM Trail walker untuk mengakses dan menyelidiki melalui aplikasi. STEM Trails memungkinkan eksplorasi komprehensif ilmu pengetahuan, teknologi, teknik, dan matematika melalui sarana fisik atau virtual. Studi ini menyarankan bahwa STEM Trails dapat berfungsi sebagai pendekatan pembelajaran matematika yang menggabungkan kegiatan outdoor dengan teknologi digital dan mengintegrasikan mata pelajaran lainnya. Math Trails adalah konsep tradisional yang telah digabungkan secara inovatif dengan teknologi digital dan berbagai kegiatan. Metode ini harus diperluas dan disempurnakan dengan menyesuaikan dengan keadaan, kondisi, dan persyaratan tertentu, dan dijalankan dalam pengaturan yang berbeda.
Implementation of NXT 2.0 Mindstorm Robot Sensors on Mobile Education for Students Kuncoro, Rizki Danang Kartiko; Arifudin, Riza; Sugiharti, Endang
Proceeding ISETH (International Summit on Science, Technology, and Humanity) 2018: Proceeding ISETH (International Summit on Science, Technology, and Humanity)
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/iseth.2289

Abstract

In the current 4.0 industry era, technological development is very fast and fast. In the world of education the lessons about technology should have been introduced to students since elementary school. Do not have to use complex technology, enough to use robotics technology from the NXT 2.0 LEGO minstorm robot which is lego-based to play and learn algorithms in composing technology. Using this tool can also be controlled by the smartphone application. Using a mobile application that we designed will make it easier for students to use and play this educational media. In this media, each sensor in the robot will be interrelated to the mobile control, we have tested this control with 83.33% detection accuracy. So that it can effectively become an interactive and fun technology-based learning media for students. The purpose of this educational media is to improve the quality of education in Indonesia so that it can be technology-based and enjoyable for students. Because the application of technology to education is very important to hone the power of creative thinking in composing programming algorithms using robots. Students will be very interested and have good enthusiasm in learning robotics based on this mobile application.
Implementasi E-Ujian Sebagai Sistem Penilaian Pembelajaran Daring di SMP Islam Roudlotus Saidiyyah Semarang Hakim, M. Faris Al; Sugiharti, Endang; Alamsyah, Alamsyah; Arifudin, Riza; Abidin, Zaenal; Putra, Anggyi Trisnawan
Jurnal Abdi Negeri Vol 2 No 1 (2024): Januari 2024
Publisher : Informa Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63350/jan.v2i1.18

Abstract

The presence of the COVID-19 outbreak has led to the implementation of online learning from the location of each home. SMP Islam Roudlotus Saidiyyah Semarang has transformed learning by utilizing various applications. However, for the purposes of final semester assessment or integrated assessment, an online exam application is needed that is easy to use and able to provide data on student learning outcomes accurately and quickly. The implementation method consists of preparation, training, and evaluation. The results of the training showed that the E-Ujian application as an application for online assessment has the potential to be applied at SMP Islam Roudlotus Saidiyyah Semarang. The utilization of the E-Ujian Application in learning activities in the partner environment is an effort to maintain the quality of learning.
Application of Fine-Tuning on Convolutional Neural Networks to Improve Classification Accuracy of Feline Skin Diseases Syahputra, Syafa Nabilah; Arifudin, Riza
Jurnal Penelitian Pendidikan Vol. 43 No. 1 (2026): April 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jpp.v43i1.40702

Abstract

Skin conditions are among the most frequent reasons for veterinary visits, yet they remain notoriously difficult to distinguish by eye alone. For the average pet owner or general practitioner, overlapping visual symptoms between diseases like ringworm and scabies often lead to diagnostic uncertainty. This study addresses this challenge by developing an automated classification system based on the MobileNetV2 architecture. By employing a two-stage transfer learning strategy, where initial feature extraction is followed by targeted fine-tuning of layers from index 100 onwards, we adapted a general-purpose model to the specific nuances of veterinary dermatology. Our results indicate a significant performance leap: while standard training struggled with the complexities of skin textures, the fine-tuned model achieved a validation accuracy of 92%. These findings suggest that fine-tuning is not just a technical optimization, but a necessary step in making deep learning a viable, accessible tool for real-world veterinary diagnostics.
Application of Fuzzy Logic in Visual Novel Evaluation System Using Unity 3D Memoriano, Epafraditus; Arifudin, Riza
Recursive Journal of Informatics Vol. 3 No. 1 (2025): March 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v3i1.1158

Abstract

Purpose: Visual novels, narrative-driven games focused on character interaction, commonly employ point-based evaluation systems that struggle to represent the inherent complexity and uncertainty of player choices. This research introduces a novel approach: integrating fuzzy logic into visual novel evaluation systems using Unity 3D. Fuzzy logic addresses the limitations of point-based systems by accounting for the "fuzzy" nature of player choice and its varied impact on story progression and character relationships. Methods/Study design/approach: A visual novel game was developed in Unity 3D, incorporating a fuzzy logic evaluation system for scoring player choices and assessing route progress. Fuzzy sets and membership functions were defined for key aspects like emotional response, character alignment, and plot development. These aspects were dynamically evaluated based on player dialogue selection, and individual scores were aggregated to generate a final route evaluation. Result/Findings: Testing demonstrated seamless integration of the fuzzy logic system within the game engine. Evaluation of conversation choices and route progression yielded accurate and nuanced scores, reflecting the varying weight of each decision based on narrative context and character interaction. Fuzzy logic facilitated the interpretation of "fuzzy" player choices, translating them into meaningful information for story progression and character relationships. Novelty/Originality/Value: This research presents a novel and promising approach to visual novel evaluation by leveraging the strengths of fuzzy logic. It overcomes the limitations of traditional point-based systems, capturing the complexity and dynamism of player choices within the narrative. The dynamic and responsive evaluation results enhance player engagement and provide a more immersive gaming experience.
Implementation of the Term Frequency-Inverse Document Frequency Method for Mental Health Classification Using Algorithm Support Vector Machine Minatika, Ilfa; Arifudin, Riza
Recursive Journal of Informatics Vol. 3 No. 2 (2025): September 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v3i2.1921

Abstract

Abstract. Mental health is a person's emotional, social and psychological condition. A person's mental health level can be influenced by emotional experiences, behavior, environment and family educational background. A person's psychological well-being can be influenced by a person's behavior, where they live, the education they receive, and their emotional experiences. It is important not to underestimate the existence of mental health disorders because the number of cases is currently increasing. Purpose: Using SVM algorithm and TF-IDF method can produce good accuracy for classification text. Therefore this research aims to determine the implementation of the use of the TF-IDF method and the SVM algorithm in mental health classification and to determine the accuracy results of using these methods. Study Method/Design/Approach: The methods used in the research this for the mental health classification is Term Frequency-Inverse Document Frequency used in the vectorization process to convert text into a numerical representation, as well as using the Support Vector Machine algorithm in modeling. The dataset used is the Mental Health Corpus dataset obtained from the Kaggle website. This dataset consists of two classes containing text and labels totaling 27,977 data. Before applying the model, preprocessing is carried out first, namely cleaning the text using stopword removal and stemming. After cleaning the text, the next process is vectorization using CountVectorizer and TF-IDF. Results/Findings: In this study the SVM algorithm was used four kernels, namely the linear kernel, the RBF kernel, the polynomial kernel, and the later sigmoid kernel get the best accuracy results on the RBF kernel if compared to with other kernels. Accuracy results obtained _ of 92.62%, value precision of 92.64%, value recall 92.62%, and value f1-score 92.62%. Novelty/Originality/Value: So, it can be concluded that the application of the SVM algorithm and the TF-IDF method is possible used for classification mental health results mark high accuracy.
Implementation of Raita Algorithm in Manado-Indonesia Translation Application with Text Suggestion Using Levenshtein Distance Algorithm Sekartaji, Novanka Agnes; Arifudin, Riza
Recursive Journal of Informatics Vol. 2 No. 2 (2024): September 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/pkvgtg90

Abstract

Abstract. Manado City is one of the multidimensional and multicultural cities, possessing assets that are considered highly potential for development into tourism and development attractions. The current tourism assets being developed by the Manado City government are cultural tourism, as they hold a charm and allure for tourists. Hence, a communication tool in the form of a translation application is necessary for facilitating communication between visiting tourists and the native community of North Sulawesi, even for newcomers who intend to reside in North Sulawesi, given that the Manado language serves as the primary communication tool within the community. This research employs a combination of the Raita algorithm and the Levenshtein distance algorithm in its creation process, along with the confusion matrix method to calculate the accuracy of translation results using the Levenshtein distance algorithm with a text suggestion feature. The research begins by collecting a dataset consisting of Manado language vocabulary and their translations in Indonesia language, sourced from literature studies and original respondents from North Sulawesi, which have been validated by a validator to prevent translation data errors. The subsequent stage involves preprocessing the dataset, converting the entire content of the dataset to lowercase using the case folding process, and removing spaces at the start and end of texts using the trim function. Next, both algorithms are implemented, with the Raita algorithm serving for translation and the Levenshtein distance algorithm providing text suggestions for typing errors during the translation process. The accuracy results derived from the confusion matrix calculations during the translation process of 100 vocabulary words, accounting for typing errors, indicate that the Levenshtein distance algorithm is capable of effectively translating vocabulary accurately and correctly, even in the presence of typing errors, resulting in a high accuracy rate of 94,17%. Purpose: To determine the implementation of the Levenshtein distance and Raita algorithms in the process of using the Manado-Indonesian translation application, as well as the resulting accuracy level. Methods/Study design/approach: In this study, a combination of the Raita and Levenshtein distance algorithms is utilized in the translation application system, along with the confusion matrix method to calculate accuracy. Result/Findings: The accuracy achieved in the translation process using text suggestions from the Levenshtein distance algorithm is 94.17%. Novelty/Originality/Value: This research demonstrates that the combination of the Raita and Levenshtein distance algorithms yields optimal results in the vocabulary translation process and provides accurate outcomes from the use of effective text suggestions. This is attributed to the fact that nearly all the data used was successfully translated by the system, even in the presence of typographical errors.
Fruit Freshness Detection Using Android-Based Transfer Learning MobileNetV2 Muttaqin, Irfan Fajar; Arifudin, Riza
Recursive Journal of Informatics Vol. 2 No. 1 (2024): March 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/e9h75682

Abstract

Abstract. Fruit is an important part of the source of food nutrition in humans. Fruit freshness is one of the most important factors in selecting fruit that is suitable for consumption. Fruit freshness is also an important factor in determining the price of fruit in the market. So it is very necessary to detect fruit freshness which can be done by machine. Take apples, bananas, and oranges as samples. The machine learning algorithm used in this study uses MobileNetV2 with transfer learning techniques. MobileNetV2 introduces many new ideas aimed at reducing the number of parameters to make it more efficient to run on mobile devices and achieve high classification accuracy. Transfer learning is used so that data does not need training from the start, so it only takes several networks from MobileNetV2 that have previously been trained and then retrained with a different purpose to improve accuracy results. Then the models that have been created are inserted into the application using Android Studio. Software testing is done through black box testing. Purpose: The purpose of this research is to design a machine-learning model to detect fruit freshness and then apply it to application Android smartphones. Methods/Study design/approach: The algorithm used in this study uses MobileNetV2 with transfer learning techniques. Models that have been created are inserted into the application using Android Studio. Result/Findings: The training results using MobileNetV2 transfer learning obtained an accuracy of 99.62% and the loss results obtained were 0.34%. The results of the application after testing using the black box testing method required improvements to the application and the machine learning model so that it can run optimally. Novelty/Originality/Value: Machine learning models that have been created using transfer learning MobileNetV2 are applied to Android applications so that they can be used by the public.
Stock Return Prediction Using Voting Regressor Ensemble Learning Arrohman, Ramadhan Ridho; Arifudin, Riza
Recursive Journal of Informatics Vol. 1 No. 2 (2023): September 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ntg4dt04

Abstract

Abstract. The value of return on stock prices is often used in predicting profits in the process of buying and selling shares based on the calculation of the return on investment. The calculation of the value of return on stock prices can be predicted automatically at certain periods, both weekly and daily Purpose: The problem faced is determining a good algorithm for making predictions due to fluctuating data on stock prices making it difficult to predict. Methods: The stages carried out by the researcher include the data preprocessing stage and then proceed to the Exploratory Data Analysis (EDA) stage to get a pattern from the data, followed by the modeling stage on the data. This research was developed using the Python programming language where the models used to make predictions can be obtained in real-time. Result: The results obtained in this study show that the Voting Regressor has the best model with an error rate of 0.032523 using Root Mean Square Error (RMSE). The results of this study can be further developed to automatically predict stock return values in the future.
Co-Authors Abas Setiawan Adha, Nugraha Saputra Adhitiya, Ervan Nur Adi Nur Cahyono Aditya, Rozak Ilham Aji Saputra Aji, Septiko Al Hakim, M. Faris Alamsyah - Alfatah, Abdul Muis Alfatah, Abdul Muis Amalia Fikri Utami Amin Suyitno Anggita, Anggita Anggyi Trisnawan Putra Ardhi Prabowo Arief Agoestanto Arief Broto Susilo Arif Widiyatmoko, Arif Ariska, Mega Arka Yanitama Arrohman, Ramadhan Ridho Asih, Tri Sri Noor Atikah Ari Pramesti, Atikah Ari Budi Prasetiyo, Budi Chakim, Muhamad Nur Choirunnisa, Rizkiyanti Clarissa Amanda Josaputri, Clarissa Amanda Damayanti, Angreswari Ayu Damayanti, Tiara Desy Fitria Astutianingtyas Devi, Feroza Rosalina Devi, Feroza Rosalina Dewi, Nuriana Rachmani Dian Tri Wiyanti Dwijanto Dwijanto, Dwijanto Endang Sugiharti, Endang Faozi, Faozi Farkhan, Feri Fata, Muhamad Nasrul Fata, Muhamad Nasrul Fitriana, Jevita Dwi Florentina Yuni Arini, Florentina Yuni Habaib, Taufik Nur Hakim, M. Faris Al Hani'ah, Ulfatun Hardi Suyitno Hardianti, Ririn Dwi Hariyanto, Abdul Hidayat, Kukuh Triyuliarno Hidayat, Kukuh Triyuliarno Hikmah, Al Hikmawati, Zahra Shofia Hikmawati, Zahra Shofia Ichsan, Nur Jumanto Jumanto, Jumanto Jumanto Unjung Kumalasari, Putri Laksita Kuncoro, Rizki Danang Kartiko Larasati, Ukhti Ikhsani Larasati, Ukhti Ikhsani Mashuri Mashuri Masrukan Masrukan Maulana, Bagus Surya Melissa Salma Darmawan Memoriano, Epafraditus Minatika, Ilfa Mohammad Asikin Much Aziz Muslim Mudzakir, Amat Muhammad Fariz Muttaqin, Irfan Fajar Nugroho, Ari Yulianto Nugroho, Muhammad Andi Nugroho, Prisma Bayu Pramadita, Anjar Aditya Putriaji Hendikawati Rachmawati, Eka Yuni Rachmawati, Eka Yuni Rahmanda, Primana Oky Rahmanda, Primana Oky Ratna Dewi, Novi Rizki Nor Amelia Rochmad - Rofik Rofik, Rofik S.Pd. M Kes I Ketut Sudiana . Safri, Yofi Firdan Safri, Yofi Firdan Sasongko, Andry Scolastika Mariani Sekartaji, Novanka Agnes Setiawan, Danang Aji Stephani Diah Pamelasari Subarkah, Agus Subhan Subhan Sukmadewanti, Irahayu Sukmadewanti, Irahayu Susanto, Febri Syahputra, Syafa Nabilah Trihanto, Wandha Budhi Trihanto, Wandha Budhi Utami, Hamdan Dian Jaya Rozi Hyang Utami, Hamdan Dian Jaya Rozi Hyang Wibowo, Eric Adie Widyawati, Kharisa Yahya Nur Ifriza Yulianto, Muhamad Maulana Yulianto, Muhamad Maulana Zaenal Abidin Zulfikar Adi Nugroho, Zulfikar Adi