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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Dinamik Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Paradikma: Jurnal Pendidikan Matematika JURNAL PENELITIAN SAINTIKA ELEMENTARY SCHOOL JOURNAL PGSD FIP UNIMED Jurnal Daya Matematis Jurnal Informatika dan Teknik Elektro Terapan Seminar Nasional Informatika (SEMNASIF) Jurnal IPTEK JURNAL PENGABDIAN KEPADA MASYARAKAT Jurnal KARISMATIKA Bina Insani ICT Journal JURNAL SAINS INDONESIA Indonesian Journal of Artificial Intelligence and Data Mining INTECOMS: Journal of Information Technology and Computer Science Jurnal Cendekia : Jurnal Pendidikan Matematika Jurnal Perspektif M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika JATI (Jurnal Mahasiswa Teknik Informatika) Community Development Journal: Jurnal Pengabdian Masyarakat Budapest International Research and Critics in Linguistics and Education Journal (Birle Journal) Informatics and Digital Expert (INDEX) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Soft Computing Exploration Djtechno: Jurnal Teknologi Informasi Jurnal Pengabdian kepada Masyarakat Teknik: Jurnal Ilmu Teknik dan Informatika INCODING: Journal of Informatics and Computer Science Engineering J-Intech (Journal of Information and Technology) Economic Reviews Journal PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Riset Rumpun Matematika dan Ilmu Pengetahuan Alam (JURRIMIPA) Jurnal Umum Pengabdian Masyarakat (JUPEMAS) Journal of Informatics and Data Science (J-IDS) Jurnal KALANDRA Journal of Education Transportation and Business International Journal of Educational Insights and Innovations (IJEDINS) Ulil Albab
Hermawan Syahputra
Universitas Negeri Medan
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Development of Cooperative Learning Tools Type Course Review Horay and Geogebra Media to Improve Spatial Thinking Skills and Mathematical Resilience of Grade VIII Students Siregar, Putri Mayang Sari; Syahputra, Hermawan; Fauzi, KMS. Amin
PARADIKMA: JURNAL PENDIDIKAN MATEMATIKA Vol. 16 No. 2 (2023): PARADIKMA JURNAL PENDIDIKAN MATEMATIKA (July - December 2023)
Publisher : Study Program of Mathematics Education of Unimed Postgraduate Program

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/paradikma.v16i2.48947

Abstract

This study aims to investigate the improvement and development of spatial thinking skills and mathematical resilience abilities of students using cooperatively developed learning tools of the Course Review Horay and Media Geogebra type; to investigate the validity, practicability, and efficacy of cooperatively developed learning tools of the Course Review Horay and Media Geogebra type in enhancing spatial thinking skills and resilience. This type of research is development research based on the ADDIE model. 33 MTs Al-Washliyah Tembung students participated in the study. The results demonstrated that 97% of the students, or 32 out of 33, improved their spatial reasoning skills. While only 19 of 33 students, or 58%, exhibited an increase in mathematical resilience. In addition, the results indicate that this development model is more effective than conventional classroom learning models at enhancing spatial reasoning and mathematical resilience. The learning aids created using cooperative Course Review Horay type and Geogebra are valid, applicable, and efficient.Keywords: ADDIE, Spatial Thinking Skills, Mathematical Resilience, Development Research
Classification of Purple Passion Fruit Ripeness Levels Using Convolutional Neural Network (CNN) Siregar, Mochammad Gani Alfa Alkhoiri; Said Iskandar Al Idrus; Hermawan Syahputra; Insan Taufik; Kana Saputra S
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.v5i2.1787

Abstract

Passiflora edulis Sims (purple passion fruit) is a fruit that offers numerous health benefits and possesses high economic value. However, the manual assessment of ripeness by traders tends to be subjective and inconsistent, leading to post-harvest losses of up to 50%. This study developed a classification model for determining the ripeness level of purple passion fruit using a Convolutional Neural Network (CNN) and implemented it in a web-based application. The CNN model was designed to classify four ripeness stages (unripe, half-ripe, ripe, and rotten) with the addition of a non-passion-fruit class to enhance the system’s robustness. The dataset consisted of 2,000 images divided into five classes: four ripeness levels of purple passion fruit (unripe, half-ripe, ripe, and rotten) and one non-passion-fruit class as a comparator. All images were in JPG and PNG formats. The CNN architecture comprised four convolutional layers with 16, 32, 64, and 128 filters, respectively. Evaluation of various data-splitting ratios (80:20, 70:30, 60:40) and learning rates (0.001, 0.0001, 0.01) showed that the optimal configuration was achieved at a ratio of 80:20 with a learning rate of 0.001, resulting in a training accuracy of 96.72% and a testing accuracy of 95.76%, with a loss value of 0.1811. Validation using 5-Fold Cross Validation produced an average accuracy of 95.40%. The model was integrated into a web application developed using Flask and JavaScript, deployed on the PythonAnywhere cloud platform, enabling users to upload images and automatically obtain ripeness predictions to assist traders in sorting fruits more quickly and accurately.
ANALISIS EFEKTIVITAS RUANG WARNA HSV DAN LAB DALAM PENGELOMPOKAN KONDISI DAUN TEH MENGGUNAKAN K-MEANS UNTUK PENENTUAN TINGKAT KEMATANGAN Affandi, Ridho; Nafiisah, Syti Salwaa; Buulolo, Calvin Sahputra; Gultom, Shaqila Rahmayani; Syahputra, Hermawan
Djtechno: Jurnal Teknologi Informasi Vol 7, No 1 (2026): April
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v7i1.8548

Abstract

Penentuan tingkat kematangan daun teh merupakan aspek krusial dalam proses sortasi bahan baku yang berpengaruh langsung terhadap mutu produk akhir. Proses identifikasi kematangan yang dilakukan secara visual oleh manusia enderuna bersifat subiektif dan rentan terhadap inkonsistensi. Oleh karena itu diperlukan pendekatan berbasis pengolahan citra digital untuk memperoleh hasil yang lebih objektif dan terstandar. Penelitian ini bertujuan untuk menganalisis efektivitas ruang warna HSV dan Lab dalam pengelompokan kondisi daun teh menggunakan algoritma K-Means untuk penentuan tingkat kematangan. Tahapan penelitian meliputi akuisisi citra daun teh, praproses citra, <onversi ruang warna dari RGB ke HSV dan Lab, ekstraksi fitur warna, serta proses klasterisasi menggunakan algoritma K-Means. Evaluasi kinerja dilakukan dengan membandingkan hasil pengelompokan terhadap label referensi yang diperoleh melalui penilaian pakar. Hasil penelitian menunjukkan bahwa kedue ruang warna mampu merepresentasikan karakteristik visual daun teh secara memadai dalam proses pengelompokan tingkat kematangan, dengan perbedaan kinerja yang ditunjukkan pada tingkat separabilitas klaster dan konsistensi hasil pengelompokan. Temuan ini diharapkan dapat menjadi dasar dalam bengembangan sistem pendukung keputusan untuk klasifikasi kematangan daur teh berbasis pengolahan citra digital
Perancangan Sistem Penerjemah Bahasa Isyarat bagi Tunarungu dan Tunawicara Berbasis Pengolahan Citra Digital dan Text-to-Speech Trianto, Nafil Rizq; Wijaya, Alfarizi; Pardede, Arion; Pandiangan, Daniel; Syahputra, Hermawan
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 6 No. 1 (2026): Mei : Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v6i1.1156

Abstract

Communication is an essential human right, yet a significant communication gap persists between individuals with sensory disabilities, specifically the deaf and speech-impaired, and the general public. While many technological solutions have been proposed to translate sign language, existing models primarily rely on heavy deep learning architectures such as Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN/LSTM). These models often demand high computational power, leading to latency and limiting real-time application on standard devices. This study proposes a lightweight, fast, and highly responsive sign language translation system specifically designed to recognize static alphabets (A-Z) and single-character air writing. The system utilizes MediaPipe for hand tracking, where feature extraction is intelligently processed by calculating the relative spatial coordinates of fingertips to the wrist, reducing dependency on raw camera coordinates. Classification is performed using a Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel, prioritizing computational efficiency without sacrificing accuracy. To enhance user experience, the system introduces three key novelties: smart relative feature extraction, an anti-duplication hold system with a 1-second timer to prevent input spamming, and a non-blocking multithreaded audio execution (Daemon Thread) utilizing Google Text-to-Speech (gTTS), ensuring the webcam feed remains fluid during audio playback. Additionally, an alternative air-writing mode is integrated, utilizing geometric heuristics and PyTesseract OCR to read single drawn letters in the air. The results indicate that the proposed system operates swiftly and efficiently, bridging the communication barrier with a hardware-friendly approach.
BOOTCAMP TEKNIK JARINGAN TELEKOMUNIKASI FIBER OPTIK UNTUK SISWA/I TKJ SMKS TRI SAKTI LUBUK PAKAM Dedy Kiswanto; Hermawan Syahputra; Suvriadi Panggabean; Sri Dewi; Nurul Maulida Surbakti
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 6 No. 2 (2025): Volume 6 No. 2 Tahun 2025
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v6i2.43050

Abstract

Kegiatan bootcamp teknik teknik jaringan telekomunikasi fiber optik untuk siswa/siswi teknik komputer dan jaringan di SMKS Tri Sakti Lubuk Pakam bertujuan untuk meningkatkan kompetensi siswa/i jurusan Teknik Komputer dan Jaringan (TKJ) di SMKS Tri Sakti Lubuk Pakam terkait instalasi jaringan fiber optik. Kegiatan ini diadakan untuk menjawab kebutuhan industri yang terus berkembang, di mana instalasi fiber optik menjadi standar dalam jaringan telekomunikasi secara Global. Metode yang dilakukan meliputi training materi teori instalasi fiber optik oleh praktisi Industri, demonstrasi, dan pelatihan langsung instalasi fiber optik. Hasil dari kegiatan ini menunjukkan bahwa sebagian besar peserta mampu memahami prinsip dasar fiber optik, jenis kabel yang digunakan, dan teknik instalasi yang benar. Namun, masih terdapat beberapa peserta yang belum sepenuhnya memahami aspek-aspek teknis tertentu. Diakhir kegiatan dilakukan penyerahan alat instalasi fiber optik kepada sekolah dengan harapan dapat mendukung peningkatan kompetensi instalasi fiber optik lebih lanjut dan memastikan kesiapan siswa menghadapi dunia kerja pada bidang telekomunikasi fiber optik.
Analisis Identifikasi Buah Jeruk Menggunakan Metode Warna RGB pada Aplikasi Berbasis Web Triwanti Andini Hutasoit; Claudia Agatha Br. Tarigan; Jonathan Rio Gultom; Hermawan Syahputra
Informatics and Digital Expert (INDEX) Vol. 8 No. 1 (2026): INDEX, Mei 2026
Publisher : LPPM Universitas Perjuangan Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36423/index.v8i1.2761

Abstract

Penelitian ini bertujuan untuk mengembangkan aplikasi berbasis web yang dapat digunakan untuk mengidentifikasi kualitas buah jeruk secara otomatis menggunakan analisis warna RGB. Permasalahan yang diangkat dalam penelitian ini adalah proses identifikasi kualitas buah yang masih dilakukan secara manual, sehingga kurang efisien dan berpotensi menimbulkan subjektivitas. Metode yang digunakan adalah pengolahan citra digital dengan memanfaatkan nilai warna RGB sebagai dasar dalam proses klasifikasi. Sistem dirancang untuk menerima input berupa citra buah, kemudian memproses dan mengklasifikasikan kondisi buah ke dalam kategori segar, matang, dan busuk. Hasil pengujian menunjukkan bahwa sistem mampu mengidentifikasi kualitas buah dengan tingkat akurasi mencapai 100% pada data uji yang digunakan. Selain itu, waktu respon sistem berada pada rentang 1.1 hingga 1.3 detik, yang menunjukkan bahwa proses deteksi dapat dilakukan secara cepat dan konsisten. Dengan demikian, sistem yang dikembangkan dapat menjadi solusi alternatif dalam membantu proses identifikasi kualitas buah secara otomatis, lebih cepat, dan lebih objektif dibandingkan metode manual.
Decision Support System Using the Analytical Hierarchy Process Method in Determining Credit Recipient Eligibility Erika Nia Devina Br Purba; Arnita; Hermawan Syahputra; Lasker P Sinaga; Adidtya Perdana
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2256

Abstract

Banks play a fundamental role in improving public welfare by collecting funds through savings and redistributing them as credit. Although credit is the primary source of bank revenue, it carries significant risks if the feasibility analysis of prospective borrowers is flawed, potentially leading to non-performing loans that disrupt financial stability. BPR Nusantara Bona Pasogit 17 faces this challenge as it currently lacks an automated decision support system, resulting in assessments that are often inconsistent or subjective. This research aims to develop a web-based decision support system using the Analytical Hierarchy Process (AHP) method to determine credit recipient eligibility. Developed using PHP and MySQL, the system incorporates criteria management, AHP calculation processing, and automated eligibility ranking. Comprehensive validation through black-box and white-box testing confirmed that all functional components performed correctly with consistent "PASS" results. The AHP implementation produced a Consistency Ratio (CR) of 0.03797, indicating high reliability in decision-making. Criterion priority weights were identified as: Income (0.386), Character (0.219), Loan Amount (0.162), Collateral (0.103), Loan Term (0.07), and Age (0.06). System testing on 100 customer records resulted in a maximum eligibility score of 0.93501 and a minimum of 0.41839.
Eye Disease Classification System Based on Fundus Images Using the InceptionV3 Architecture Annisa Aulia; Hermawan Syahputra; Yulita Molliq Rangkuti; Insan Taufik; Kana Saputra S
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2263

Abstract

This study aims to develop an automated eye disease classification system based on retinal fundus images using the InceptionV3 deep learning architecture. The dataset consists of four classes: cataract, diabetic retinopathy, glaucoma, and normal, collected from public sources and clinical data. The proposed method applies several preprocessing techniques, including background segmentation, data augmentation, data normalization, and an 80:20 data split to improve model performance and generalization. Transfer learning is implemented by utilizing pretrained ImageNet weights and modifying the final layers to suit the classification task. The model is trained using the Adam optimizer with a learning rate of 0.001 and categorical cross-entropy loss function. Evaluation results show that the model achieves an accuracy of 96%, with average precision, recall, and F1-score values of 0.97, 0.96, and 0.97, respectively. The confusion matrix analysis indicates that most predictions are correctly classified, demonstrating strong performance across all classes. Furthermore, the model is successfully integrated into a web-based system that enables users to upload fundus images and obtain classification results automatically. These findings indicate that the proposed system can effectively assist in early detection of eye diseases and support clinical decision-making.
Smart Absen Implementation of a Facial Recognition-Based Student Attendance System Using the Haar Cascade Method and LBPH Frengki Alfredo Matondang; Sahara Lani Lestari; Dinda Syafitri; Kayla Amelia Putri; Hermawan Syahputra
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2301

Abstract

Manual attendance systems in higher education institutions are often hampered by inefficiency, data inaccuracy, and vulnerability to fraud such as proxy attendance. This study presents the design and implementation of Absen Smart, a face recognition-based attendance system developed using the Haar Cascade and Local Binary Pattern Histogram (LBPH) algorithms within the React.js and Flask frameworks. This system enables the automatic and real-time identification of students via a webcam without requiring additional hardware. Face detection is performed using the Haar Cascade classifier from OpenCV, while face recognition uses the LBPH Face Recognizer with a confidence threshold of 50. Testing was conducted with 28 registered students from the Computer Science Program at UNIMED, Class A, 2024 cohort. Functional evaluation results show that all seven core system features—including face detection, face recognition, duplicate prevention, automatic absence tracking, and Excel report generation—were successfully executed with a 100% success rate. The system achieved a facial recognition accuracy of 92.86%, with an average processing time of 1.2 seconds per verification. These results indicate that the proposed system is an effective, practical, and scalable solution for automating academic attendance in a university setting.
Prediksi Penjualan Produk Makanan dan Minuman Ringan pada PT. Sinar Niaga Sejahtera Menggunakan Metode Holt-Winters Berbasis Website M. Revano Ananda Lubis; Insan Taufik; Said Iskandar Al Idrus; Arnita Arnita; Hermawan Syahputra
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.1024

Abstract

PT. Sinar Niaga Sejahtera is a food and beverage distribution company that still relies on conventional methods to determine stock levels, often facing inventory management challenges due to fluctuations in market demand. This study aims to predict sales of the 15 best-selling products at the Tebing Tinggi branch using the Holt-Winters method, based on a website that provides historical sales data from January 2021 to December 2024. The research stages include problem identification, data collection, application of the Holt-Winters method, model evaluation using Mean Absolute Percentage Error (MAPE), and implementation of a website-based system. The results of the study on one product, Garuda Atom Original, show optimal parameters of α = 0.1, β = 0, and γ = 0.8 with an MAPE value of 5,703115%, which is classified as very good. The implementation of a website-based sales prediction system makes it easier for administrators to manage product data, record sales data, and obtain prediction results in the form of informative graphs and tables, thereby helping the company reduce the risk of overstocking or understocking and supporting more effective data-driven decision-making.
Co-Authors Abil Mansyur, Abil Ade Amelia, Tasya Adhi Guna, Ekin Adidtya Perdana, Adidtya Affandi, Ridho Agus Harjoko Ahmad Andi Solahuddin Ahmad Hidayat Ajizah Siregar Aldiva Wibowo Alfattah Atalarais Amelia Br Siregar, Ririn Amelia Vega S. Meliala, Ruth Ami . Riana Andani D N Andika Maulana, Sandy Angel Tumanggor, Asri Angginy Akhirunisa Siregar Ani Sutiani Annisa Aulia Apiek Gandamana Arnita Arnita Arnita Asrin Lubis BORNOK SINAGA Bornok Sinaga Budi Akbar, Muhammad Buulolo, Calvin Sahputra citra Claudia Agatha Br. Tarigan Daulay, Leni Karmila Davina, Sherly Dedy Husrizal Syah, Dedy Husrizal Dedy Kiswanto Defiyanti, Aqilah Delvin Ibo, Martince Deo Demonta Panggabean Dhea Putri Adriani DIdi Febrian Dina Aulia Luthfiah Dinda Kartika Dinda Syafitri Drilanang, Mhd Ilyasyah Dwi Zahra Putri, Raisya E. Elvis Napitupulu E. Elvis Napitupulu, E. Elvis Edi Syahputra Edward Perdana Sinaga Elisabet Butarbutar, Lastri Erika Nia Devina Br Purba Fanny Rahmadani Farmawaty Tambunan, Vivielda Fauzi, KMS. Amin Fransiska Sihombing, Esra Frengki Alfredo Matondang Gultom, Shaqila Rahmayani Hafiz, Alvin Harefa, Meilinda Suriani Hasratuddin Siregar Hidayatul Arifin, Muhammad Husna Batubara, Shabrina Ida Ayu Putu Sri Widnyani Ihsan Zulfahmi Ihsan Zulfahmi Ika Purnama Sari Imelda, Yusmita Impana Manik, Kristin Indriani.S, Dechy Deswita Insan Taufik Irhamna Irhamna Irmaya, Nia Irya Shakila Syukron, Ananda Iwan Jepri Izwita Dewi Jonathan Rio Gultom Josafat Simanjutak, Todo Kana Saputra S Karimuddin Hakim Hasibuan Kayla Amelia Putri Khairun Nadiah Kms. Amin Fauzi Lasker Pangarapan Sinaga Lazuardi Harahap, Muhammad Luge, Miclyael Luthfiah, Dina Aulia M. Ari Maulana M. Revano Ananda Lubis Mahyuni Mahyuni Martina Restuati Maulana, Raihan Maya Oktora MHD. Reza M.I. Pulungan Mia Yolanda Siregar Muhammad Febrilian Zulrahman Mukti Hamjah Harahap, Mukti Hamjah Nafiisah, Syti Salwaa Nasution, Dinda Indriani Nico Pasaribu, Michael Niska, Debi Yandra Nova Yanti Panjaitan Nur Wahyuni Nurmala Berutu Nurul Maulida Surbakti Oktavia, Grace Palendeo Sitepu, Kalpin Pandiangan, Daniel Pane, M Iqbal Anata Pane, Yeremia Yosefan Panggabean, Suvriadi Panjaitan, Clara Kresensia Panjaitan, Nova Yanti Pardede, Arion Permata Putri Pasaribu, Yohanna Prana Walidin, Adamsyach Purba, Boy Hendrawan Purba, Desni Paramitha Putri Mayang Sari Putri Mayang Sari Siregar R Givent A Simanjorang Ramadhan Manik, Albert Ramadhani, Fanny Rambe, Imelda Wardani Rangkuti, Muhammad Aswin Riana, Ami Richi, Alfina Sahara Lani Lestari Said . Iskandar SANTI MARIA SIMARMATA Santi Maria Simarmata Sembiring, Rinawati Sinaga, Elya Juni Arta Siregar, Mochammad Gani Alfa Alkhoiri Siregar, Putri Mayang Sari Siti Nabila Panjaitan Solahudin, Ahmad Andi Sri Dewi Sriadhi Sriadhi, Sriadhi Steven Imanuel Naibaho Sukma, Ayman Human Suleho, Febrina Suvriadi Panggabean Syamsah Fitri Syarief Afifi Sumantri Syawal Gultom Tri Bowo Atmojo Trianto, Nafil Rizq Triwanti Andini Hutasoit Veryawan, Veryawan Waliyul M Siregar Warjaya, Angga Wibowo, Aldiva Wijaya, Alfarizi winsyahputra Ritonga Yazid Noor, Muhammad Yulita Molliq Rangkuti Zul Amry Zulfahmi Indra, Zulfahmi Zulfahrizan, Atta