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All Journal KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Jurnal Informatika dan Rekayasa Elektronik Jurnal Informatika dan Rekayasa Perangkat Lunak J-AKSI : JURNAL AKUNTANSI DAN SISTEM INFORMASI Journal of Artificial Intelligence and Engineering Applications (JAIEA) AMMA : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika dan Teknologi Informasi
Edi Tohidi
STMIK IKMI Cirebon
Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing Social Sciences

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Pelatihan Dasar Microsoft Office Bagi Remaja Putus Sekolah Sebagai Upaya Pemberdayaan Digital Edi Tohidi; Dodi Solihudin; Mochamad Arief Saputra; Mochammad Haris Maulana Ibrahim
AMMA : Jurnal Pengabdian Masyarakat Vol. 2 No. 3 (2023): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Basic skills in using Microsoft Office applications are essential competencies for enhancing job opportunities and participation in various activities in the digital era, especially for out-of-school youth who often have limited access to formal education. This Community Partnership Program aims to empower out-of-school youth through basic Microsoft Office training. This training is designed to provide understanding and practical skills in using Microsoft Word for document processing, Microsoft Excel for data processing and simple calculations, and Microsoft PowerPoint for creating presentations. It is expected that, through this training, out-of-school youth can improve their functional skills, open opportunities for jobs requiring basic administrative abilities, and increase their self-confidence in facing the challenges of the digital era.
Pengembangan Aplikasi Kasir Android Bagi Pelaku Usaha Mikro Edi Wahyudin; Edi Tohidi; Muhamad Yoni Ardiansah; Muhamad Agastya
AMMA : Jurnal Pengabdian Masyarakat Vol. 2 No. 3 (2023): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

The use of Android-based cashier applications can improve the efficiency and accuracy of transactions for micro-entrepreneurs. This Community Partnership Program aims to provide training on creating simple Android-based cashier applications for micro-entrepreneurs. This training covers the introduction to the basics of Android application development using a specific platform, the design of an intuitive user interface (UI) for sales transactions, the implementation of key cashier application features such as transaction recording, calculation of total purchases, and simple report generation. It is expected that, through this training, micro-entrepreneurs can have the ability to create cashier applications that suit their business needs, thereby improving operational efficiency and financial management.
Peningkatan Layanan RT Melalui Sistem Informasi Administrasi Berbasis Web Dodi Solihudin; Edi Tohidi; Abi Fajar Ahmad Fauzi; Ade Valentino
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 03 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Administrative services at the neighborhood level (Rukun Tetangga/RT) are an essential component in supporting good governance within communities. However, in practice, these services are still frequently managed manually, leading to various issues such as service delays, data entry errors, and inefficiencies in documentation. This Community Service Program (PKM) aims to design and implement a web-based neighborhood administrative information system to assist RT administrators in delivering faster, more accurate, and transparent services to residents. The implementation methods include needs assessment, system design, software development, as well as training and technical assistance for both administrators and residents. The system is developed using web-based technologies (PHP, MySQL, and HTML/CSS), allowing access via computers or smartphones. Key features of the system include resident data management, automated issuance and printing of official letters, archive management, and financial and activity reporting. The implementation results indicate a significant improvement in the efficiency of RT administrative services. RT administrators are no longer burdened with manual record-keeping, and residents can access services independently from their homes. Moreover, the system supports administrative transparency, as all activities are digitally recorded and easily traceable. This program has a positive impact on digital literacy among residents and strengthens the integration of information technology with public services at the micro community level. In the future, the system is expected to be replicated in other RTs as a community-based digital transformation solution.
Peningkatan Literasi Keuangan Keluarga melalui Pelatihan Digital Pasca Pandemi Edi Tohidi; Edi Wahyudin; Adhivia Julian; Aditya Fauzi Samsuri
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 03 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

The COVID-19 pandemic has had a significant impact on household economic stability in Indonesia. Many families have experienced a decrease in income, changes in consumption patterns, and limited access to formal financial services. In the post-pandemic period, the economic challenges faced by households have become increasingly complex, thus requiring enhanced literacy and skills in financial management, particularly those based on digital tools. This Community Service Program (PKM) aims to provide training in digital-based household financial management to communities in the partner area, in order to improve their ability to manage income, expenses, savings, and the wise use of digital financial applications. The implementation method includes an initial survey to assess participants' financial literacy levels, preparation of training modules, face to-face and online training sessions, as well as evaluation of participants’ improvement in knowledge and skills. The training materials cover topics such as household financial planning, debt and savings management, the use of digital wallets, financial recording applications, and basic understanding of micro-investments. The results of the program indicate that the training successfully increased participants’ understanding of basic financial concepts and improved their ability to use digital financial tracking applications. Participants also became more skilled in preparing family budgets, monitoring daily expenses, and identifying priority needs. Other positive outcomes include heightened awareness of the importance of saving and managing financial risk. This activity provides tangible contributions to fostering economic self-reliance among families in the digital era. Going forward, similar training programs are expected to be developed as sustainable initiatives, particularly for lower-middle income communities that are more vulnerable to economic shocks.
PENINGKATAN MODEL KLASIFIKASI SENTIMEN PENGGUNA APLIKASI TOMORO COFFEE MENGGUNAKAN ALGORITMA NAÏVE BAYES Dina Audina; Ade Irma Purnamasari; Agus Bahtiar; Edi Tohidi
Jurnal Informatika dan Rekayasa Elektronik Vol. 8 No. 1 (2025): JIRE APRIL 2025
Publisher : LPPM STMIK Lombok

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Abstract

Kemajuan teknologi informasi telah merevolusi cara bisnis berinteraksi dengan pelanggan melalui aplikasi mobile, termasuk dalam sektor makanan dan minuman. Aplikasi Tomoro Coffee menghadapi tantangan dalam mempertahankan kepuasan pengguna akibat keterbatasan fitur dan masalah teknis. Penelitian ini bertujuan untuk menerapkan algoritma Naïve Bayes guna meningkatkan model klasifikasi sentimen ulasan pengguna, menganalisis distribusi sentimen positif dan negatif beserta faktor utama yang memengaruhinya, serta mengevaluasi performa model berdasarkan akurasi, presisi, recall, dan F1-score. Data ulasan dikumpulkan dari Google Play Store dan diolah menggunakan metode Knowledge Discovery in Database (KDD), yang mencakup pembersihan data, tokenisasi, penghapusan stopword, stemming, serta ekstraksi fitur menggunakan Term Frequency-Inverse Document Frequency (TF-IDF). Hasil penelitian menunjukkan bahwa algoritma Naïve Bayes mencapai akurasi sebesar 90%, dengan presisi 91,3%, recall 87,3%, dan F1-score 88,7%. Temuan ini memberikan wawasan strategis bagi pengembang aplikasi dalam meningkatkan layanan dan fitur berdasarkan analisis sentimen pengguna. Dari hasil analisis, 64,4% ulasan tergolong positif, didominasi oleh komentar seperti "kopinya enak", sementara 35,6% ulasan negatif umumnya berisi keluhan teknis, seperti "tidak tersedia".
OPTIMASI KLASTERISASI PENERIMAAN PAJAK BUMI DAN BANGUNAN MENGGUNAKAN ALGORITMA K-MEDOIDS Febri Abdi Annur Dhuha; Ade Irma Purnamasari; Denni Pratama; Edi Tohidi; Edi Wahyudin
JURNAL AKUNTANSI DAN SISTEM INFORMASI Vol 7 No 1 (2026): Edisi Februari 2026
Publisher : Program Studi Akuntansi Fakultas Ekonomika dan Bisnis Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/j-aksi.v7i1.16659

Abstract

Pajak Bumi dan Bangunan (PBB) merupakan komponen strategis dalam Pendapatan Asli Daerah (PAD) yang berperan penting dalam mendukung penyelenggaraan pembangunan dan pelayanan publik. Namun, heterogenitas data dan variasi karakteristik objek pajak menyebabkan pemerintah daerah mengalami kesulitan dalam memetakan potensi fiskal dan tingkat kepatuhan wajib pajak secara akurat. Penelitian ini bertujuan menganalisis efektivitas algoritma K-Medoids dalam mengelompokkan wajib pajak di Kecamatan Tanjung berdasarkan atribut numerik, yaitu luas tanah, luas bangunan, NJOP tanah, NJOP bangunan, dan nilai PBB tahun berjalan. Metode penelitian meliputi tahapan pengumpulan data, pra-pemrosesan, transformasi logaritmik, normalisasi, implementasi algoritma K-Medoids, serta evaluasi hasil klaster menggunakan metrik Silhouette Coefficient dan Davies–Bouldin Index. Proses komputasi dilakukan menggunakan Python dengan pustaka pyClustering dan scikit-learn. Hasil penelitian menunjukkan terbentuknya empat klaster wajib pajak dengan karakteristik berbeda: klaster aset besar berkontribusi rendah, klaster premium berkontribusi tinggi, klaster ekonomi rendah dengan pola pembayaran tidak stabil, dan klaster ekonomi menengah dengan kepatuhan cukup baik. Evaluasi kualitas model menghasilkan Silhouette Coefficient sebesar 0,4204 dan Davies–Bouldin Index sebesar 0,7893, yang menunjukkan struktur klaster cukup baik dan stabil. Temuan ini memberikan kontribusi empiris dalam mendukung optimalisasi pengelolaan PBB berbasis analitik, serta dapat digunakan sebagai dasar penyusunan strategi penagihan berbasis prioritas dan formulasi kebijakan fiskal yang lebih tepat sasaran.
Optimization of Convolutional Neural Networks Using Resizing Techniques for Banana Leaf Disease Classification Aldiyansyah Kurniawan; Ade Irma Purnamasari; Denni Pratama; Edi Tohidi; Edi Wahyudin
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1876

Abstract

Early and accurate identification of banana leaf diseases is essential for supporting digital agriculture, as visual symptoms often require rapid and reliable analysis. This study investigates the impact of three image resizing techniques squashing, letterboxing, and random resized crop on the performance of the MobileNetV2 architecture in classifying four categories of banana leaf images using the Banana Leaf Disease Dataset v4 consisting of 4,675 samples. The experiments were conducted using a transfer learning approach with an 80:10:10 data split, standardized normalization, and data augmentation. The results show that all resizing techniques achieved test accuracies above 92%. Squashing produced the highest accuracy and fastest training time, letterboxing demonstrated the most stable performance with the lowest validation loss, and random resized crop improved generalization to variations in object position. These findings confirm that resizing strategies significantly influence the stability and effectiveness of CNN models. Overall, MobileNetV2 proves capable of delivering accurate and efficient classification of banana leaf diseases when supported by an appropriate preprocessing pipeline. This study provides empirical evidence for developing image-based plant disease diagnosis systems within smart agriculture.
Improving the Education Development Contribution Payment Model at SMK Istiqomah Maruyung Using the C4.5 Algorithm Noviyanti; Ade Irma Purnamasari; Agus Bahtiar; Edi Tohidi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 3 (2025): June 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i3.729

Abstract

  Payment of tuition fees is one of the important aspects of school financial management. At SMK Istiqomah Maruyung, the management of SPP payments is still done manually, which causes student non-compliance in paying on time. The purpose of the research is to improve the SPP payment model by using the C4.5 algorithm to classify the level of student compliance and identify the main factors that influence late payments. The method used is the Knowledge Discovery in Databases (KDD) approach which includes the stages of data selection, preprocessing, transformation, data mining, and result evaluation. The research data was taken from 206 students in the 2023/2024 academic year with attributes such as parental income, number of siblings, scholarship status, and academic grade point average. The C4.5 algorithm was applied to build a decision tree model, with evaluation using five-fold cross validation. The result of this study is that the C4.5 algorithm is able to classify student compliance levels with an average accuracy of 93.55%. The main factors that influence late payment are academic grade point average, class, and parental income. Although the model is very good at predicting compliant students (precision 95%, recall 98%), it shows weakness in predicting lateness (precision 67%, recall 40%). It is concluded that the C4.5 algorithm can improve the efficiency of managing tuition payments and provide data-driven insights for policy making. With further implementation, this algorithm is expected to be adopted by other educational institutions to address similar challenges in financial management.
Sentiment analysis to classify TikTok Shop Users on Twitter with Naïve Bayes Classifier Algorithm Ayu Lestari; Ade Irma Purnamasari; Agus Bahtiar; Edi Tohidi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.748

Abstract

Advances in information technology have facilitated the use of social media as an e-commerce platform, with TikTok Shop enabling in-person transactions. This research addresses the gap in understanding user perceptions of TikTok Shop through sentiment analysis on Twitter. Sentiment classification is performed using the Naïve Bayes Classifier algorithm. The dataset consists of 1,907 Indonesian tweets, collected from January 2023 to July 2024, and processed using RapidMiner in the Knowledge Discovery in Database (KDD) framework. The preprocessing stages include data cleaning, normalization, tokenization, stopword removal, and stemming. To overcome data imbalance, Synthetic Minority Oversampling Technique (SMOTE) was applied. The model achieved 93.98% accuracy, with balanced precision and recall for positive, neutral, and negative sentiments. The sentiment distribution among TikTok Shop users on Twitter was 35.5% positive, 35.5% negative, and 29.0% neutral. This research provides insights into consumer behavior on social media and emphasizes the importance of sentiment analysis to increase user engagement and understand market perception. This research is expected to provide information to platform developers and businesses looking to improve TikTok
K-Means Algorithm for Grouping Models of Dengue Fever Prone Areas in Cirebon City Aida Safitri; Ade Irma Purnamasari; Agus Bahtiar; Edi Tohidi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.834

Abstract

Dengue hemorrhagic fever (DHF) is an infectious disease transmitted through the Aedes aegypti mosquito. DHF cases in Cirebon City show a significant increase every year. This study aims to classify dengue prone areas based on case data per health center in 2020-2024 obtained from the Cirebon City Health Office. The method used is the K-Means algorithm with the Knowledge Discovery in Database (KDD) approach, which includes data selection, preprocessing, data transformation, data mining, evaluation, and knowledge. Evaluation using Davies-Bouldin Index (DBI) showed optimal results at k = 6 with a DBI value of -0.445. The clustering results produced six clusters: cluster 5 (437 dengue cases in 34 health centers) showed high risk; cluster 0 (244 cases), cluster 2 (129 cases), and cluster 3 (279 cases) showed medium risk; while cluster 1 (69 cases) and cluster 4 (86 cases) showed low risk. This study shows that the K-Means algorithm is effective in identifying DHF risk distribution patterns and provides a strategic basis for the Cirebon City Health Office to prioritize interventions and develop more effective prevention strategies.
Co-Authors Abi Fajar Ahmad Fauzi Ade Irma Purnamasari Ade Valentino Adhivia Julian Adi Kaswadi Aditya Fauzi Samsuri Agus Surip Aida Safitri Aldiyansyah Kurniawan Alwan Azhar Andi Ardiansyah Anita Nur Kirana Ayu Lestari Bahrul Ulum Denni Pratama Dina Audina Dodi Solihudin Eddiwan Dwiguna Edi Wahyudin Fadhil Muhammad Basysyar Fajrina Putri Salsabila Febri Abdi Annur Dhuha Gifthera Dwilestari Gina Regiana Hegarmanah Muhabatin Hilya Ashfia Nabila Ika Anikah Iqbal Agis Junizar Irfan Ali Lian Siliayani Mochamad Arief Saputra Mochammad Haris Maulana Ibrahim Muhamad Agastya Muhamad Yoni Ardiansah Muhammad Aji Pratama Muhammad Harun Al- Musa Nela Puji Rahayu Nisa Dieanwati Nuris Noviyanti Rachmat Trikar Sohadi Raena Agustin Laeliyah Revita Lestari Faujiah Saeful Anwar Shifa Dwi Oktaviani Siti Aisyah Tuti Hartati Uyu Nuraeni Yudhistira Arie Wijaya Yuniar Kartika