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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) 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 Dharma Indonesia Jurnal Abdi Negeri
Riza Arifudin
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Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Peningkatan Pemahaman Sains, Coding, dan Robotik Berbasis STEM untuk Guru Matematika dan IPA SMP Kota Semarang Arif Widiyatmoko; Arka Yanitama; Riza Arifudin; Stephani Diah Pamelasari; Melissa Salma Darmawan; Desy Fitria Astutianingtyas; Aji Saputra
Journal of Community Empowerment Vol 3 No 1 (2023): Journal of Community Empowerment
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jce.v3i1.72574

Abstract

Abstrak. Kegiatan ini dilatarbelakangi oleh guru Matematika dan IPA Kota Semarang yang belum pernah mengimplementasikan sains, coding, dan robotik berbasis STEM (Science, Technology, Engineering, and Mathematic) pada pembelajaran. Padahal sains, coding dan robotik berbasis STEM merupakan pengetahuan dan keterampilan yang dapat mendukung keterampilan abad ke-21 dan era revolusi industri 4.0. Tujuan pengabdian ini adalah meningkatkan pemahaman sains, coding, dan robotik berbasis STEM untuk guru Matematika dan IPA Kota Semarang. Metode yang digunakan dalam kegiatan pengabdian ini adalah pemaparan materi oleh narasumber, praktik merancang robot sederhana dan pengkodingan melalui aplikasi, yakni Scratch for Arduino (S4A), serta sesi tanya jawab dan diskusi. Rata-rata pemahaman peserta pelatihan terhadap sains, coding, dan robotik berbasis STEM pada pretest adalah 49,71%, sedangkan pada postest adalah 83,94%. Sehingga, diperoleh N-gain sebesar 0,52 dengan kriteria sedang. Hasil tersebut mengandung arti bahwa terjadi peningkatan pemahaman peserta terhadap sains, coding, dan robotik berbasis STEM setelah diadakannya pelatihan. Peserta memiliki respon positif terhadap penyampaian materi pada pelatihan dengan skor rata-rata persentase sebesar 92,08%. Persentase tersebut mengandung arti bahwa pelatihan sains, coding, dan robotik berbasis STEM telah berlangsung dengan sangat baik. Abstract. This activity was motivated by Mathematics and Science teachers in Semarang City who had never implemented STEM (Science, Technology, Engineering, and Mathematic)-based science, coding, and robotics in learning. Whereas STEM-based science, coding and robotics are knowledge and skills that can support 21st century skills and the industrial revolution 4.0 era. The purpose of this training is to increase STEM-based understanding of science, coding, and robotics for Mathematics and Science teachers. The method used in this training is presentation of material by speakers, practice of designing simple robots and coding through applications, namely Scratch for Arduino (S4A), as well as question and answer sessions and discussions. The average understanding of the trainees on STEM-based science, coding, and robotics at the pretest was 49.71%, while at the posttest it was 83.94%. Thus, an N-gain of 0.52 is obtained with moderate criteria. These results imply that there was an increase in participants' understanding of STEM-based science, coding, and robotics after the training was held. Participants had a positive response to the delivery of material at the training with an average percentage score of 92.08%. This percentage means that the STEM-based science, coding and robotics training has been going very well.
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/rji.v2i1.70845

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.
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/rji.v2i2.73651

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.
Enhancing service excellence: analyzing natural language question answering with advanced cosine similarity Arifudin, Riza; Subhan, Subhan; Ifriza, Yahya Nur
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1773-1781

Abstract

Information related to student services in higher education must be produced and disseminated in various forms. Covid-19 pandemic, student services with a remote model related to this question and answer become very important. To carry out this automation process, the advanced cosine similarity method is used to check the similarity of the questions to the database and statistics to calculate the similarity value of each word. The proposed paper proceeds with three phases. The first stage to solve this problem is the data processed in question; the professional next step is word insertion. It converts alphanumeric words to vector format. Each word is a vector that represents a point in space with a certain dimension. The recommended advanced cosine similarity data still must be analyzed into a statistical approach. We will measure accuracy to get results so that optimal results and answers are obtained, research procedures are carried out based on literature study, initial data collection and observation, system development, system testing, system analysis, and system evaluation. This research implemented in universities with student chat automation applications providing an accuracy 83.90% given by natural language question answering system (NLQAS) so that it can improve excellent service in universities.
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/rji.v1i2.68048

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.
Soft voting ensemble model to improve Parkinson’s disease prediction with SMOTE Unjung, Jumanto; Rofik, Rofik; Sugiharti, Endang; Alamsyah, Alamsyah; Arifudin, Riza; Prasetiyo, Budi; Muslim, Much Aziz
International Journal of Advances in Intelligent Informatics Vol 11, No 1 (2025): February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i1.1627

Abstract

Parkinson's disease is one of the major neurodegenerative diseases that affect the central nervous system, often leading to motor and cognitive impairments in affected individuals. A precise diagnosis is currently unreliable, plus there are no specific tests such as electroencephalography or blood tests to diagnose the disease. Several studies have focused on the voice-based classification of Parkinson's disease. These studies attempt to enhance the accuracy of classification models. However, a major issue in predictive analysis is the imbalance in data distribution and the low performance of classification algorithms. This research aims to improve the accuracy of speech-based Parkinson's disease prediction by addressing class imbalance in the data and building an appropriate model. The proposed new model is to perform class balancing using SMOTE and build an ensemble voting model. The research process is systematically structured into multiple phases: data preprocessing, sampling, model development utilizing a voting ensemble approach, and performance evaluation. The model was tested using voice recording data from 31 people, where the data was taken from OpenML. The evaluation results were carried out using stratified cross-validation and showed good model performance. From the measurements taken, this study obtained an accuracy of 97.44%, with a precision of 97.95%, recall of 97.44%, and F1-Score of 97.56%. This study demonstrates that implementing the soft-voting ensemble-SMOTE method can enhance the model's predictive accuracy.
Peran Kecerdasan Buatan Generatif Bagi Peningkatan Kompetensi Guru di SMA Muhammadiyah 2 Semarang Setiawan, Abas; Arifudin, Riza; Sugiharti, Endang; Abidin, Zaenal; Al Hakim, M. Faris; Choirunnisa, Rizkiyanti; Subarkah, Agus
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 2 (2025): MEI 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i2.2952

Abstract

Tantangan yang saat ini dibutuhkan oleh guru SMA adalah menciptakan inovasi pembelajaran berbasis teknologi. Saat ini kecerdasan buatan (AI) telah muncul sebagai solusi potensial untuk meningkatkan kualitas pembelajaran di tengah pesatnya kemajuan teknologi. Namun, geografi Indonesia yang luas membuat fasilitas teknologi pendidikan belum merata. Oleh karena itu, guru di SMA Muhammadiyah 2 Semarang perlu meningkatkan kompetensi literasi digitalnya, terutama untuk teknologi terkini. Program ini telah berhasil membuka wawasan guru terhadap teknologi baru dan memberikan keterampilan praktis dalam mengintegrasikan teknologi Kecerdasan Buatan Generatif ke dalam proses pembelajaran. Para guru diberikan pembekalan penggunaan teknologi ChatGPT dan Gemini untuk mempersiapkan bahan ajar. Hasil evaluasi menunjukkan bahwa guru mampu memahami dan mulai menerapkan teknologi AI dalam pembuatan materi ajar, serta merasa termotivasi untuk terus menggunakannya secara berkelanjutan dalam pembelajaran.
Pendampingan Pemanfaatan Aplikasi Berbasis Kecerdasan Buatan bagi Guru di Yayasan Waqah Al Hidayah, Hatyai, Provinsi Songkhla, Thailand Abidin, Zaenal; Arifudin, Riza; Sugiharti, Endang
Jurnal Inovasi Pengabdian dan Pemberdayaan Masyarakat Vol 5 No 1 (2025): JIPPM - Juni 2025
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jippm.758

Abstract

Pengabdian kepada masyarakat ini bertujuan meningkatkan literasi digital guru melalui pemanfaatan teknologi kecerdasan buatan (AI) dalam pembelajaran di Yayasan Waqaf Al-Hidayah, Hatyai, Thailand. Tantangan penggunaan teknologi di kalangan guru di Hatyai, Provinsi Songkhla menjadi perhatian utama, khususnya dalam konteks pembelajaran yang relevan bagi generasi digital. Metode kegiatan mencakup lima tahapan: identifikasi kebutuhan, pengenalan AI, praktik penggunaan agen cerdas berbasis Large Language Model (LLM), pengembangan media pembelajaran berbasis Canva AI, serta pendampingan dan monitoring melalui media sosial. Hasil kegiatan menunjukkan bahwa seluruh peserta telah menggunakan aplikasi berbasis AI sebelumnya, dan menganggap AI membantu efisiensi dan kreativitas pembelajaran. Namun, kekhawatiran terhadap ketergantungan dan dampak sosial juga muncul. Evaluasi melalui angket mengungkapkan respons positif terhadap kegiatan, meskipun partisipasi terbatas akibat kendala geografis. Simpulan dari kegiatan ini adalah bahwa pendampingan pemanfaatan AI dapat memperkuat kompetensi guru, namun tetap diperlukan penguatan etika penggunaan teknologi untuk menjaga kualitas interaksi dan keberimbangan peran guru dalam proses pembelajaran.
Student Adaptability Level Optimization using GridsearchCV with Gaussian Naive Bayes and K-Nearest Neighbor Methods as an Effort to Improve Online Education Predictions Arifudin, Riza; Subhan, Subhan; Ifriza, Yahya Nur
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.88972

Abstract

This increase in adaptability is sought through the application of optimization techniques using the Gaussian Naive Bayes and K-Nearest Neighbor (KNN) methods. This research utilizes GridSearchCV to find optimal parameter configurations in both methods. The Gaussian Naive Bayes method will be used to analyze and classify student adaptability patterns based on historical data. In addition, the K-Nearest Neighbor (KNN) method will be used to utilize information from students who have similar characteristics to increase prediction accuracy. The main steps of this research involve collecting student adaptability data from online education sources, processing the data to obtain relevant features, and using GridSearchCV to find the best parameters in the Gaussian Naive Bayes and KNN models. By optimizing the prediction model using the GridSearchCV technique, this research is expected to make a significant contribution to improving the quality of online education, creating a more adaptive learning environment, and helping educational institutions in designing appropriate learning models. The Receiver Operating Characteristic (ROC) curve also showed a superior Area Under the Curve (AUC) score for KNN at 0.89, compared to GNB 0.81, confirming that the optimized KNN model offers significantly better sensitivity and specificity in predicting student adaptability levels in online education.
Optimalisasi Kompetensi Pedagogi Guru MTs Assalaam Kota Kartasura melalui Inovasi Model Pembelajaran untuk Menguatkan Literasi Siswa Ratna Dewi, Novi; Nor Amelia, Rizki; Aji, Septiko; Arifudin, Riza; Damayanti, Tiara; Anggita, Anggita
Jurnal Dharma Indonesia Vol. 3 No. 2 (2025): Jurnal Dharma Indonesia
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jdi.v3i2.27854

Abstract

This community service activity was carried out in response to the low level of student literacy and the limited implementation of innovative learning models at Madrasah Tsanawiyah (MTs) Pondok Pesantren Modern Islam (PPMI) Assalaam. MTs PPMI Assalaam is a boarding Islamic educational institution that integrates the national and pesantren curricula to develop graduates who are religious, academically competent, and of strong character. Through a collaborative-participatory approach, this training and mentoring program was designed to enhance teachers’ pedagogical competencies, particularly in applying literacy-based learning models within the TPACK (Technological Pedagogical Content Knowledge) framework. The program was conducted in three stages (preparation, implementation, and evaluation), and involved 59 teachers as participants. The results showed a significant improvement in content mastery, particularly regarding deep learning principles, digital literacy, partnership focus, and critical reasoning (p<0.05). Final evaluations indicated that the training was well-received, with high participant satisfaction and effective methods for strengthening both competence and innovative teaching practices. This program has proven effective in addressing 21st-century literacy challenges within the modern pesantren context, while fostering an adaptive, collaborative, and Islamically-rooted learning ecosystem
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 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 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 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