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All Journal JURNAL SISTEM INFORMASI BISNIS Techno.Com: Jurnal Teknologi Informasi Jurnal Inovasi Teknologi Pendidikan Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnas Nasional Teknologi dan Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING InComTech: Jurnal Telekomunikasi dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING JSiI (Jurnal Sistem Informasi) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Building of Informatics, Technology and Science Journal of Information Systems and Informatics Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia Internet of Things and Artificial Intelligence Journal Journal of Information Technology and Cyber Security CSRID Journal of Informatics and Interactive Technology (JIITE)
Imam Tahyudin
Program Studi Sistem Informasi STMIK AMIKOM Purwokerto
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Public Health

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Perbandingan Model CNN, LSTM, dan FNN dalam Klasifikasi Kulit Penderita Diabetes samsul; tahyudin, imam; setyo utomo, fandy
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 3 (2025): JPTI - Maret 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.536

Abstract

Penelitian ini mengembangkan dan membandingkan kinerja tiga algoritma Deep Learning, yaitu Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), dan Feedforward Neural Networks (FNN), untuk mengklasifikasi gambar kulit penderita diabetes dan kulit sehat. Data yang digunakan terdiri dari gambar kulit yang diproses melalui tahapan pra-pemrosesan, pembangunan model, pelatihan, dan evaluasi. Parameter yang diuji meliputi akurasi klasifikasi masing-masing model. Hasil menunjukkan ba hwa LSTM mencapai akurasi tertinggi sebesar 94%, diikuti oleh CNN dengan 87%, dan FNN dengan 82%. Model terbaik diimplementasikan dalam aplikasi berbasis web menggunakan Flask, yang dapat memberikan prediksi otomatis untuk mendukung diagnosis dini. Penelitian ini berkontribusi pada pengembangan teknologi diagnostik yang dapat membantu mencegah komplikasi serius pada pasien diabetes melalui deteksi dini kondisi kulit.
Implementasi Teknologi Deep Learning untuk Diagnostik Stroke Otak Berbasis CNN-LSTM-FNN Nur holifah, Anggita; Tahyudin, Imam
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 3 (2025): JPTI - Maret 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.538

Abstract

Stroke otak merupakan salah satu penyebab utama kematian dan kecacatan di dunia, dengan dampak besar pada sistem kesehatan dan ekonomi global. Penelitian ini bertujuan untuk mengembangkan model prediksi dini stroke otak berbasis deep learning dengan mengintegrasikan algoritma Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), dan Feedforward Neural Network (FNN). Dataset yang digunakan terdiri atas citra medis seperti CT scan dan MRI, data temporal, serta informasi klinis lainnya, yang diproses menggunakan teknik preprocessing dan augmentasi data. CNN berfungsi untuk mengekstraksi fitur dari citra medis, LSTM untuk menganalisis data sekuensial, dan FNN untuk mengolah data terstruktur. Hasil penelitian menunjukkan bahwa CNN mencapai akurasi tertinggi sebesar 97%, diikuti oleh LSTM dengan 94%, dan FNN sebesar 70%. Integrasi ketiga algoritma ini menghasilkan model prediksi yang lebih akurat dan komprehensif dibandingkan pendekatan individual. Pendekatan ini berpotensi meningkatkan akurasi diagnosis stroke, mempercepat pengambilan keputusan medis, serta mendukung pengelolaan perawatan pasien yang lebih efisien, sehingga dapat mengurangi beban pada sistem kesehatan global.
Sistem Klasifikasi Perintah Suara Menggunakan Artificial Neural Network Pada Aplikasi G-MOOC 4D Setiabudi, Rizki; Utomo, Fandy Setyo; Tahyudin, Imam
Journal of Informatics and Interactive Technology Vol. 1 No. 1 (2024): April
Publisher : ACSIT

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63547/jiite.v1i1.5

Abstract

Speech recognition atau pengenalan suara merupakan salah satu cabang ilmu di bidang Natural Language Processing (pengeloaan bahasa alami) yang berfokus pada pengembangan teknologi yang memungkinkan komputer untuk mengenali suara manusia seperti perintah suara, speech to text dan transkripsi otomatis. Teknologi speech recognition saat ini berkembang pesat untuk dimanfaatkan dalam teknologi informasi. Selain itu, teknologi informasi memiliki peranan penting dalam pembelajaran online. Pada kasus G-MOOC yaitu Platform Massive Open Online Course yang dibangun menggunakan pendekatan gamifikasi. Akan tetapi pada aplikasi G-MOOC tersebut masih belum ramah terhadap disabilitas kususnya tunanetra. Dengan demikian diperlukan sebuah sistem untuk melakukan pembelajaran yang ramah disabilitas untuk memudahkan melakukan proses pembelajaran. Untuk mengatasi masalah tersebut, dibuatkan sebuah aplikasi pengembangan dari G-MOOC (Gamified Massive Open Online Course) menjadi G-MOOC 4D (Gamified Massive Open Online Course for Disability) menggunakan sistem perintah suara dengan mengklasifikasikan kata menggunakan artificial neural network. Metode dalam penelitian ini diantaranya adalah identifikasi masalah, pengumpulan data (data primer dan studi pustaka), metode penelitian (pra-pemrosesan data, pelatihan model, penerapan model ann ke speech recognition dengan web speech api), pengujian dan evaluasi. Hasil dari penelitian ini berdasarkan hasil pengujian blackbox memperoleh akurasi sebesar 100% dengan menggunakan rate recognition average.
Penerapan CNN dan RNN untuk Pembuatan Deskripsi Konten Visual Menggunakan Deep Learning Hermanto, Aldy Agil; Karyono, Giat; Tahyudin, Imam
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6958

Abstract

The development of technology in the field of image and sound processing has had a significant impact on increasing the accessibility of information for various groups, especially for individuals with visual impairments. One of the innovations that emerged was the image to speech system, which allows the conversion of images into sounds that can be understood by its users. The main problem lies in the low accuracy of object recognition in images with high variability, such as poor lighting or complex backgrounds, as well as the challenge of producing suitable text descriptions to be converted into audio. The method used involves extracting image features using InceptionV3-based CNN and forming a sequence of descriptive texts through RNN with an attention mechanism. The dataset consists of 40,455 captions and 8,091 images, processed using text and image pre-processing techniques before being trained using the teacher forcing technique. The evaluation results show a very low BLEU score (5.154827976372712e-153), indicating the model's inability to replicate the original caption well. However, the audio from the text-to-speech conversion using Google Text-to-Speech is quite clear. Future solutions include increasing the dataset, applying regularization, and adjusting the model architecture to improve the accuracy of caption prediction and audio relevance to the image. With these improvements, it is hoped that the system can provide more inclusive visual information accessibility for individuals with visual impairments.
Menjelajahi Tantangan dan Kemajuan Dalam Deep Learning Untuk Readmisi Pasien: Tinjauan Literatur Sistematis Surur, Miftahus; Tahyudin, Imam; Saputra, Dhanar Intan Surya; Nanjar, Agi
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 5 (2025): JPTI - Mei 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.681

Abstract

Prediksi readmisi pasien telah menjadi tantangan utama dalam meningkatkan kualitas layanan kesehatan. Penelitian ini menyajikan tinjauan sistematis terhadap algoritma deep learning, dengan menganalisis 30 artikel dari database utama seperti Scopus, IEEE Xplore, dan ScienceDirect. Proses pencarian literatur dilakukan menggunakan kombinasi kata kunci seperti 'deep learning', 'readmisi pasien', dan 'prediksi kesehatan' serta mengikuti kerangka PRISMA untuk menyaring studi yang relevan berdasarkan kriteria inklusi dan eksklusi. Hasil penelitian menunjukkan bahwa algoritma Long Short-Term Memory (LSTM) mendominasi dalam menangkap pola temporal dari data Electronic Health Record (EHR), dengan kinerja mencapai Area Under the Curve (AUC) hingga 88,4%. Selain itu, Convolutional Neural Networks (CNN) terbukti efektif untuk menganalisis teks tidak terstruktur, sementara model Transformer menunjukkan potensi dalam menangani dataset berskala besar. Tantangan utama yang ditemukan meliputi ketidakseimbangan data dan heterogenitas data medis, yang dapat mempengaruhi akurasi prediksi. Solusi inovatif seperti federated learning dan Explainable AI (XAI) diusulkan untuk meningkatkan interpretabilitas dan efisiensi algoritma dalam konteks klinis. Penelitian ini memberikan wawasan berharga mengenai potensi dan keterbatasan deep learning dalam prediksi readmisi pasien serta menawarkan rekomendasi strategis untuk pengembangan teknologi kesehatan yang lebih baik.
Analisis Sentimen Ulasan Pengguna Aplikasi Sinaga Mobile pada Google Play Store Menggunakan Algoritma Naive Bayes Iskoko, Angga; Tahyudin, Imam; Purwadi, Purwadi
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 6 (2025): JPTI - Juni 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.843

Abstract

Dalam era digital, aplikasi berbasis teknologi memiliki peran penting dalam meningkatkan efisiensi layanan publik. Aplikasi Sinaga Mobile dikembangkan untuk membantu administrasi kepegawaian bagi Pegawai Negeri Sipil (PNS), namun masih terdapat berbagai keluhan pengguna terkait kinerjanya. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna terhadap aplikasi ini dengan menggunakan algoritma Naïve Bayes. Data dikumpulkan melalui teknik scraping dari Google Play Store, dengan total 1003 ulasan. Setelah melalui tahapan preprocessing yang mencakup cleaning, normalisasi, tokenizing, filtering, dan stemming, data diklasifikasikan menggunakan model Naïve Bayes. Hasil penelitian menunjukkan bahwa dari 1003 ulasan, 235 sentimen positive (23,42%) dan 768 sentimen negatif (76,57%), dengan permasalahan utama terkait fitur presensi dan stabilitas sistem. Model yang digunakan menunjukkan hasil akurasi 83 %. Dengan hasil penelitian  ini, pengembang aplikasi dapat memperoleh wawasan mengenai aspek yang perlu diperbaiki guna meningkatkan kepuasan pengguna. Selain itu, penelitian ini juga membuktikan efektivitas metode pembelajaran mesin dalam menganalisis opini pengguna secara sistematis.
User Satisfaction Analysis Of Tourist Ticket Applications Using The Heuristic Evaluation Evaluation Method (Case Study: Pagubugan Melung Tour Manager) Evania Adna; Tahyudin, Imam
CSRID (Computer Science Research and Its Development Journal) Vol. 16 No. 3 (2024): October 2024
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.16.3.2024.13-26

Abstract

The development of digital technology has changed the way information is interacted with and managed, including in the tourism sector. Nature tourism, as an important part of Indonesia's tourism industry, has also undergone a significant transformation in terms of technology use. This research investigates the application of information technology through websites in improving the accessibility, efficiency, management, and promotion of Pagubugan Melung Tourism in Banyumas. By adopting a website-based ticketing application, Wisata Pagubugan Melung is able to provide more complete and interactive information for visitors and simplify the process of ticket sales and visitor management. Evaluation of user acceptance of the ticketing application was conducted using the Heuristic Evaluation method and Partial Least Square Equation Model (PLS-SEM) statistical analysis. The variables that become indicators in evaluating user acceptance of ticketing applications using PLS-SEM statistical analysis are Visibility of System Status, Match Between System and the Real World, User Control and Freedom, Consistency and Standards, Error Prevention, Recognition Rather Than Recall, Flexibility and Efficient of Use, Aesthetic and Minimalist Design, Help Users Recognize, Diagnose, and Recover from Errors, Help and Documentation and Usability. The results showed that most features were well received by users. Overall, the Wisata Pagubugan Melung ticketing app is popular, showcasing the blend of technology and tourism. This research provides a better understanding of user acceptance of technological innovations in the context of tourism and provides guidance for further development in improving technology-based tourism services.
PENERAPAN TEKNIK HEURISTIK UNTUK MENINGKATKAN REKOMENDASI PRODUK DARI DATA TRANSAKSI PELANGGAN DI TOKO JAFFAMART MENGGUNAKAN QUERY SQL Al-Haq, Ahnaf Vanning Al-Haq; Tahyudin, Imam
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 1 (2025): Februari 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.1.2025.50-70

Abstract

In today's digital era, understanding customer purchase patterns has become crucial to enhancing the shopping experience and building brand loyalty. Jaffamart, as a provider of daily necessities, generates transaction data rich in valuable information. However, managing large and complex data often presents challenges in making effective decisions. This study adopts a heuristic approach to filter relevant data and optimize SQL queries to ensure fast and efficient data access. This research aims to apply heuristic techniques and SQL queries in a product recommendation system based on Jaffamart's transaction data. The methodology involves analyzing transaction data and applying heuristic techniques to identify frequent purchase patterns made by customers. These techniques help enhance the relevance of product recommendations provided. The results show that the combination of heuristic techniques and SQL query optimization not only accelerates the analysis process but also improves the accuracy and efficiency of product recommendations to customers. By using this method, Jaffamart can provide a more personalized and relevant shopping experience for each customer. This study makes a significant contribution to the development of data-driven business strategies in the retail industry. It is hoped that the findings of this research can serve as a reference for Jaffamart and other stores to optimize their product recommendation systems, enhance customer experiences, and support sustainable business growth in an increasingly competitive market.
Application Of Market Basket Analysis For Sales Transaction Analysis Using Association FP-Growth Algorithm Wini Audiana; Tahyudin, Imam
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 1 (2025): Februari 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.1.2025.33-49

Abstract

In an increasingly competitive business world, leveraging transaction data has become crucial for understanding consumer behavior and designing effective marketing strategies. This study aims to apply the FP-Growth algorithm in Market Basket Analysis (MBA) to identify consumer purchase patterns at KS Swalayan. The data analyzed in this research was taken from sales transactions that occurred during October 2024, with key attributes including product codes, product names, quantity, unit price, total price, and discounts. This research follows the Knowledge Discovery in Databases (KDD) framework, which includes stages of data selection, data cleaning, transformation, pattern collection, and result evaluation. The research findings indicate that the FP-Growth algorithm successfully identified significant associative relationships between various products. For example, there is a relationship between the products "Snack and Roti" and "Susu," which shows a lift value of 1.414861701, indicating a strong correlation between them. These findings provide the basis for marketing strategy recommendations such as product bundling, optimizing shelf layouts, and more efficient stock management. Additionally, the results of this study have the potential to improve consumer shopping experiences by offering products that are frequently bought together. Overall, this study highlights the effectiveness of the FP-Growth algorithm in uncovering consumer purchase patterns, which can support data-driven decision-making and improve marketing strategy efficiency in the retail sector. The implementation of this technique can serve as a valuable tool for store managers to enhance their competitiveness and business performance.
Analisis Pola Penjualan Produk Ritel Menggunakan Algoritma Apriori di Toko Reika Zulfa Ummu Hani; Tahyudin, Imam
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 1 (2025): Februari 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.1.2025.71-92

Abstract

This research aims to analyze product purchasing patterns at Toko Reika by utilizing the Apriori algorithm as a data mining method. The analysis process is conducted through a series of stages in Knowledge Discovery in Database (KDD), which includes data selection, cleaning, transformation, analysis, and evaluation. The results of this study successfully identified 36 association rules from the analyzed transactions, illustrating various combinations of related products. One of the most striking findings is the rule with the highest lift value, which is the combination of Basic Needs, Food Supplements, and Food Ingredients, with a lift value of 8.7. This indicates that these three products have a very strong correlation in consumer transactions. Additionally, the combination of Snacks, Basic Needs, and Food Ingredients also stands out, with a confidence value reaching 76%. This suggests that consumers who purchase one product from this combination are highly likely to purchase the other products as well. The analysis also reveals significant purchasing patterns within certain categories, such as Skincare, Food Supplements, and Bathing Supplies, which show high lift values and meaningful relationships between products in a single transaction. The insights gained from this research can be utilized to design data-driven marketing strategies, such as bundling promotions, product arrangement, and more effective stock management. It is hoped that these findings can help retail stores improve operational efficiency, maximize sales, and provide a better shopping experience for consumers.
Co-Authors Agustina, Nur Ngaenun Al-Haq, Ahnaf Vanning Al-Haq Alam, Yusuf Nur Alfirnanda, Weersa Talta Ammar Fauzan, Ammar Ananda, Fahesta Ananda, Rona Sepri Andrianto Andrianto Anggraini, Lintang Wahyu ANNISA HANDAYANI Anton Satria Prabuwono Arifa, Pujana Nisya Aris Munandar Azhari Shouni Barkah Bayu Surarso Berlilana Berlilana Che Pee, Ahmad Naim Daffa, Nauffal Ammar Dani Arifudin Dhanar Intan Surya Saputra Diniyati, Faoziyah Fahiya Eko Priyanto Eko Winarto Evania Adna Faiz Ichsan Jaya Fajariyanti, Alya Nur Fandy Setyo Utomo Fatmawati, Karlina Diah Febryanto, Bagas Aji Fitriani, Intan Indri Giat Karyono Hadie, Agus Nur Hellik Hermawan Hermanto, Aldy Agil Hidayah, Septi Oktaviani Nur Ilham, Rifqi Arifin Irfan Santiko Iskoko, Angga Isnaini, Khairunnisak Nur Khoerida, Nur Isnaeni khusnul khotimah Kuat Indartono Kusuma, Bagus Adhi Lestari, Silvia Windri Ma'arifah, Windiya Maulida, Trisna Melia Dianingrum Miftahus Surur, Miftahus Muhammad Reza Pahlevi Murtiyoso Murtiyoso Musyafa, Muhamad Fahmi Nabila, Putri Isma Nanjar, Agi Nazwan, Nazwan Nur Faizah Nur holifah, Anggita Oyabu, Takashi Prasetya, Subani Charis Prastyo, Priyo Agung PUJI LESTARI Purwadi Purwadi Purwadi Purwadi Putra, Bernardus Septian Cahya Putra, Feishal Azriel Arya R Rizal Isnanto Rahayu, Dania Gusmi Rahma, Felinda Aprilia Ramadani, Nevita Cahaya Rizaqi, Hanif Rozak, Rofik Abdul Rozak, Rofiq 'Abdul Rozak, Rofiq Abdul Rozak, Rofiq ‘Abdul Rozaq, Hasri Akbar Awal Saefullah, Ufu Samsul Samsul Arifin Santoso, Bagus Budi Sarmini Sarmini Satriani, Laela Jati Setiabudi, Rizki Sholikhatin, Siti Alvi Syafaat, Alif Yahya Syafiq, Bayu Ibnu Taqwa Hariguna Tikaningsih, Ades Tri Retnaningsih Soeprobowati Triana, Latifah Adi Triawan, Puas Wahyudin, Widya Cholid Wardani, Syafa Wajahtu Widiawati, Neta Tri Wini Audiana Wulandari, Hendita Ayu Yarsasi, Sri Zainal Arifin Hasibuan Zulfa Ummu Hani Zumaroh, Agnis Nur Afa