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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Informatika dan Teknik Elektro Terapan Panrita Abdi - Jurnal Pengabdian pada Masyarakat JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science (JOINTECS) Jurnal Ilmiah FIFO JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI ILKOM Jurnal Ilmiah Jiko (Jurnal Informatika dan komputer) Conference on Innovation and Application of Science and Technology (CIASTECH) JOURNAL OF SCIENCE AND APPLIED ENGINEERING Applied Technology and Computing Science Journal Jurnal Aplikasi Dan Inovasi Ipteks SOLIDITAS Systematics Buana Information Technology and Computer Sciences (BIT and CS) Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Pengabdian Masyarakat Multidisiplin Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Conference on Industrial Revolution for Polytechnic Education International Journal of Community Service (IJCS) Prosidia Widya Saintek Journal of Education and Teaching Learning Jurnal Abdimas Mahakam
Aviv Yuniar Rahman
Universitas Widyagama Malang
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Comparison of Machine Learning as an Inference Engine to Improve Expert Systems in Dengue Disease Istiadi, -; Marisa, Fitri; Joegijantoro, Rudy; Suksmawati, Affi Nizar; Rahman, Aviv Yuniar
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3192

Abstract

Dengue disease remains a significant public health challenge in tropical and subtropical regions, with rising incidence and mortality rates over the past few decades. While expert systems have been developed for early detection, traditional approaches often rely on rigid rule-based inference engines, which are limited by their dependence on expert-defined structures and lack adaptability to evolving knowledge sources. This study introduces a novel approach to enhance the flexibility and adaptability of expert systems by integrating machine learning (ML) techniques into the inference engine, leveraging the growing availability of medical record data as a dynamic knowledge source. Using a dataset of 90 medical records, balanced to 126 items via the Synthetic Minority Over-sampling Technique (SMOTE), we evaluated the performance of multiple ML algorithms, including Decision Trees (DT), Support Vector Machines (SVM), and Artificial Neural Networks (ANN), against traditional models like Naive Bayes (NB) and K-Nearest Neighbors (KNN). The DT, SVM, and ANN models demonstrated exceptional performance, achieving average accuracy, precision, recall, and F1 scores of 97.73%, 98.33%, 97.22%, and 97.41%, respectively. The key innovation of this research lies in developing an adaptive inference engine that can dynamically learn from medical data, reducing reliance on static rule bases and enabling the expert system to evolve with new knowledge. This approach improves diagnostic accuracy and provides a scalable and flexible framework for addressing other infectious diseases. By bridging the gap between expert systems and machine learning, this study paves the way for more intelligent, data-driven healthcare solutions with significant implications for public health and disease management.
Determination of Training Participants in Community Work Training Centers Using the Naïve Bayes Classifier Algorithm Hananto, April Lia; Hananto, Agustia; Huda, Baenil; Rahman, Aviv Yuniar; Novalia, Elfina; Priyatna, Bayu
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.1995

Abstract

Community work training centers are skills training institutions that aim to improve the skills of the surrounding community by providing training programs that align with industry needs. Registration of training participants at the Al-Ikhwan Islamic Boarding School community work training centers often faces obstacles, namely, the selection process is still manual, so it takes a long time, and there is a possibility of errors. This study aims to apply the Naive Bayes Classifier Algorithm to determine whether applicants pass training at the Al-Ikhwan Islamic Boarding School community work training centers. This classification method is used to help optimize the applicant selection process by considering administrative factors, income, and training quotas. RapidMiner software is used as a tool to implement the algorithm. This study found that the Naive Bayes Classifier Algorithm can provide good accuracy results in determining applicants who pass the training selection. The test results show that the resulting model has an accuracy of 90.00% in determining passing training participants with data that has the highest chance of passing, namely data that has the attributes of the female gender, age 20 years, last education Senior High School/Vocational High School, student work/student, income 364,912, father's work as laborer, father's income 3912,280, mother's work as an IRT, and mother's income 885,964. This research increases efficiency and accuracy in determining training applicants at the Al-Ikhwan Islamic Boarding School community work training centers.
Pemberdayaan kepada Kelompok Pengolah Sampah Organik sebagai Pakan Cacing di Kecamatan Sukun Kota Malang Aviv Yuniar Rahman; Feddy Wanditya Setiawan; April Lia Hananto
Jurnal Abdimas Mahakam Vol. 4 No. 1 (2020): Januari
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24903/jam.v4i1.753

Abstract

Pemberdayaan masyarakat saat ini dapat mengubah pengetahuan dan keterampilan. Di Kecamatan Sukun, Kota Malang. Di Kota Malang saat ini memiliki kelompok maupun organisasi  pengolah sampah organik menjadi pakan cacing. Kelompok ini membutuhkan informasi tentang dampak limbah, pengelolaan limbah, dan memanfaatkan limbah menjadi pundi-pundi uang. Dari keterbatasan sumber daya manusia dan keterampilan yang mereka miliki. Ternyata limbah organik dapat digunakan sebagai pakan cacing. Sehingga bisa menambah pemasukan bagi kelompok pengolah sampah organik. Dalam pelaksanaan pemberdayaan ini, dibutuhkan tahapan yang terdiri dari survei, dokumentasi, pembuatan bahan dan modul, konseling dan pelatihan, dan evaluasi. Ukur hasil pemberdayaan dengan membagikan kuesioner sebelum dan sesudah kuesioner kepada 10 peserta. Pertanyaan tentang kuesioner sebelum dan sesudah terdiri dari pengetahuan dan keterampilan. Kuesioner dibagi menjadi tiga tahap acara yang terdiri dari konseling dan pelatihan dalam manajemen keuangan, manajemen pemasaran, dan proses produksi. Hasil yang diperoleh setelah kegiatan adalah jumlah peserta yang mengalami peningkatan 80% dalam pengetahuan dari konseling manajemen keuangan. Untuk hasil konseling manajemen pemasaran memiliki peningkatan pengetahuan sebesar 90%. Sementara hasil perpanjangan di ketahui dari proses produksi mencapai peningkatan pengetahuan 95%.
Literasi Teknologi untuk Budidaya Jamur Ahmad Fauzi; Jamaludin Indra; April Hananto; Elfina Novalia; Aviv Yuniar Rahman
Jurnal Abdimas Mahakam Vol. 6 No. 02 (2022): Juli
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24903/jam.v6i02.1513

Abstract

Kabupaten Karawang memiliki lahan pertanian yang dapat mendukung budidaya jamur. Pendapatan budidaya jamur yang menjanjikan maka perlu adanya sosialiasi pemanfaatan teknologi. Pengkondisian ruangan budidaya jamur dilakukan menggunakan mikrokontroller dengan pengaturan standar ruangan budidaya jamur. Budidaya jamur befokus pada dua jenis jamur yaitu Jamur Tiram (Pleurotus Ostreatus) dan Jamur Merang yang merupakan salah satu komoditas pertanian yang memiliki nilai gizi sangat baik dan memiliki potensi yang baik untuk dikembangkan. Kegiatan dilakukan dengan penerapan teknologi mikrokontroller dan IoT dalam kumbung jamur untuk budidaya jamur merang. Literasi dilakukan kepada petani melalui sosialisasi penerapan tekologi tersebut sesuai dengan potensi manfaat Industri 4.0 mengenai perbaikan kecepatan fleksibilitas produksi. Peralatan teknologi yang diterapkan terdiri atas sensor dan actuator. Monitoring ruangan dapat terlihat melalui display LED yang menggambarkan kondisi ruang kumbung. Hasil yang diperoleh selama masa tanam 35 hari yaitu warna jamur lebih cerah, ukuran jamur lebih besar dan hasil panen lebih banyak. Kata Kunci: Budidaya jamur, Literasi teknologi, mikrokontroller, IoT, Industri 4.0.
Peningkatan Standarisasi Kualitas Pupuk dan Ubi Berbasis Sistem Cerdas Aviv Yuniar Rahman; Chauliah Fatma Putri; Adya Hermawati
Jurnal Abdimas Mahakam Vol. 7 No. 01 (2023): Abdimas Mahakam
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24903/jam.v7i01.1919

Abstract

Indonesia memiliki hasil pangan yang berlimpah dalam hasil olahannya. Salah satunya yaitu ubi jalar. Sumber karbohidrat dari ubi jalar banyak di manfaatkan sebagai bahan pangan di berbagai industri. Namun, dalam proses pemilihan ubi jalar masih menggunakan proses manual. Oleh karena itu membutuhkan tenaga yang banyak dalam proses pemilihan ubi jalar. Tidak hanya itu, dalam pengiriman ke berbagai mitra tidak memiliki standarisasi dalam mengirim yang di minta oleh industri. Sehingga banyak ubi jalar yang terbuang karena busuk ataupun mentah karena tidak dapat di olah. Berdasarkan dalam masalah tersebut, pengabdian ini bertujuan untuk memberi peningkatan sumber daya manusia. Tujuan kedua yaitu membuat sistem deteksi kualitas pupuk ubi jalar dan sistem deteksi ubi jalar berbasis sistem cerdas. Karena banyak di tempat tersebut masih minimnya pengetahuan dan keterampilan dalam memproduksi ubi jalar. Kemudian dalam segi peralatan juga dapat membuat alat deteksi kualitas pupuk yang dapat meningkatkan standarisasi dari pupuk ubi. Sehingga nantinya jika kualitas bagus jumlah produksi akan meningkat dengan adanya pupuk yang berkualitas. Pada Ubi jalar juga dapat membuat alat deteksi kualitas ubi yang nantinya dapat meningkatkan kualitas ubi dengan menghasilkan ubi dari berbagai grade untuk pengiriman ke industri. Hasil dalam pengabdian ini diketahui pengetahuan kualitas dan standarisasi kualitas pupuk terjadi peningkatan dari 60% menjadi 92%, untuk pengetahuan kualitas dan standarisasi kualitas ubi jalar mengalami peningkatan dari yang semula 67% menjadi 97% sedangkan pengetahuan dibidang sumber daya manusia juga mengalami peningkatan dengan yang sebelumnya melakukan pekerjaan tidak terstruktur, kini para pekerja sudah mulai melakukan pekerjaan sesuai dengan SOP dalam pekerjaan masing-masing.
PENINGKATAN MANAJEMEN KEUANGAN DAN PEMASARAN PADA UKM UBI JALAR BERBASIS WEB Aviv Yuniar Rahman; Chauliah Fatma Putri; Adya Hermawati
Jurnal Pengabdian Masyarakat Multidisiplin Vol 6 No 3 (2023): Juni
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/jpm.v6i3.3115

Abstract

Small and medium enterprises in Indonesia still have a lot to improve to make work easier in each field. One of them is in the field of financial management and marketing. This time, many SMEs in Indonesia still use manual marketing and financial systems. This system is said to be manual because it still uses traditional methods such as manual financial calculations, and marketing processes that must use a face-to-face system. In this case, the service team describes these problems to improve the marketing and financial system in Sweet Potato SMEs. Because many of these places still use a manual system which will later be replaced with a website-based system. Results in the concept of financial management partner previously correct 75% have increased by as much as 90%. Then from the wrong concept of financial management from the previous 25% to 10%. This is an increase in partners' knowledge of the concept of financial management. Next is the management goal, before partners can answer correctly with a level of 70% to 100%. Then the partner answered incorrectly, from the previous 30% there was a decrease to 0%. The increase in partners' knowledge of financial management objectives is considered very high because it has reached 100%. The results on the importance of marketing management from the previous partner answered correctly 85% to 100% correct. Then from the previous `15% wrong result, it became 0% wrong. This has increased because partners know the importance of marketing management. Then in determining the target market, before the partners answered correctly by 70%, it became 100%. And the previous incorrect result of 30% becomes 0%. This increase in determining the target market increases the knowledge of Sweet Potato SME partners.
Typology of Tourist Villages in Batu City Based on Cluster Analysis: Strategic Implications for Tourism Management Adya Hermawati; Armanu Thoyib; Triana Dewi Salma; Aviv Yuniar Rahman; Rizki Dwi Ariyanto; Rizki Dwi Farotul Khasanah
Journal of Education and Teaching Learning Vol.4 No.2 October (2026)
Publisher : PT MEDIA JURNAL DAN PENDIDIKAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59211/mjpjetl.v4i2.308

Abstract

Tourist villages play a strategic role in boosting local government revenue and ensuring equitable economic distribution among communities; however, uniform management approaches are often ineffective due to variations in potential and performance across villages. This study aims to identify the typology of tourist villages in Batu City based on eight tourism management variables: tourism facilities, infrastructure and accessibility, service quality, creative economy innovation, visitor satisfaction, entrepreneurial orientation, tourist visits, and competitive advantage. Primary data were collected through a questionnaire survey involving 1,152 respondents representing 384 Neighborhood Associations (RT) across 24 villages/sub-districts in three districts of Batu City, namely Batu District, Bumiaji District, and Junrejo District. Sample selection employed a proportional stratified sampling technique. The analytical methods used were a Systematic Literature Review to identify the research variables, followed by Cluster Analysis to group the villages/sub-districts based on similarities in their characteristics. The results indicate that the 24 tourist villages/sub-districts in Batu City can be classified into three clusters: Cluster 2 as the high-performing group (mean = 4.389), Cluster 1 as the moderate group (mean = 3.969), and Cluster 3 as the low-performing group (mean = 3.758). Each cluster exhibits significant differences in characteristics across all measured variables. These findings imply that development strategies for tourist villages must be differentiated and typology-based rather than uniform, ensuring that tourism management policies are more targeted and have a tangible impact on enhancing the competitive advantage of tourist villages in Batu City.  
Classification of Starling Images Using a Bayesian Network April Lia Hananto; Aviv Yuniar Rahman; Tukino Paryono; Bayu Priyatna; Agustia Hananto; Baenil Huda
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.423

Abstract

The classification of starling species is vital for biodiversity conservation, especially as some species are endangered. This research investigates the effectiveness of the Bayesian Network (BayesNet) for classifying starling species and compares its performance with Artificial Neural Networks (ANN) and Naive Bayes models. The dataset comprises 300 images of five starling species—Bali, Rio, Moon, Kebo, and Uret—captured under controlled conditions. Feature extraction focused on color, texture, and shape, while data augmentation through slight image rotations was applied to enhance model generalization. The BayesNet model achieved an accuracy of 96.29% using a 90:10 training-to-testing split, outperforming ANN (90.74%) and Naive Bayes variants. Precision, recall, F1-score, and AUC-ROC values further validated the robustness of the BayesNet model, with precision at 0.90, recall at 0.91, F1-score at 0.92, and AUC-ROC at 0.95. These results demonstrate the superior performance of multi-feature Bayesian Networks in starling classification compared to other machine learning models. The novelty of this study lies in its application of a probabilistic approach using Bayesian Networks, which enhances interpretability and performance, especially in scenarios with limited data. Future work may explore additional feature sets and advanced machine learning models to further improve classification accuracy and robustness.
Co-Authors Adi Saputra, Deni Adya Hermawati Affi Nizar Suksmawati Ahmad Farhan, Ahmad Ahmad Fauzi Anderias Bai Seran April Hananto April Lia Hananto Armanu Thoyib arrozi, lalu muhamad alawi Baenil Huda Bagus Setyawan Budiawan, Renaldi Widi Chauliah Fatma Putri Da Costa, Zania Abuk Daiman, Claudia Nila Dio Amin Putra Eko Sediyono Fauzi Ahmad Muda Firman Nurdiansyah Firman Nurdiyansyah Firman Nurdiyansyah, Firman Hananto, Agustia Hendry Hero Diogenes Adoe Huda, Baenil Ilhamsyah Ilhamsyah Indra, Jamaludin Istiadi Istiadi Istiadi Joko Purwanto khusniyatul latifah Kristianingrum Kristianingrum, Kristianingrum Luruk, Maria Ovalia Mamba’us Sa’adah Maria Alfanita Bhia, Meisya Muhammad Romadloni Putra Muslim, Shinta Nilam Sari Napulun, Kanisius Neno, Adi nisti, Melita Novalia, Elfina Nudiyansyah, Firman Nusman, Bayu Pahlevi Putra, Rangga Paryono, Tukino Priyandoko Gigih Priyatna, Bayu Putera, Rangga Pahlevi Putra, Rangga Pahlevi Putra, S.Pd., M.T., Rangga Pahlevi Putra Ratna Hidayati Rizki Dwi Ariyanto Rizki Dwi Farotul Khasanah Rudy Joegijantoro, Rudy Rukmanta Jayawiguna Sabar Setiawidayat Setiawan, Feddy Wanditya Sidi, Husri Suhada, Karya Suksmawati, Affi Nizar Syah, Adryan Syahroni Wahyu Iriananda, Syahroni Wahyu Triana Dewi Salma Tukino Ursaputra Pratama, Lionardi Wisnu, Alif Dio Raka Yuliana Rachmawati