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All Journal BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer International Journal Software Engineering and Computer Science (IJSECS) INFORMATIKA KOMMAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Journal of Creative Student Research Journal of Artificial Intelligence and Digital Business
Kahfi Heryandi Suradiradja
Universitas Pamulang
Humanities 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 Social Sciences Transportation

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PELATIHAN DESIGN GRAFIS SEDERHANA DENGAN APLIKASI CANVA BAGI SISWA SMK NASIONAL DEPOK Kahfi Heryandi Suradiradja; Dani Ramdani; Slamet Raharjo
KOMMAS: Jurnal Pengabdian Kepada Masyarakat Vol 4, No 1 (2023): KOMMAS: JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : KOMMAS: Jurnal Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Salah satu kewajiban yang dibebankan kepada dosen Universitas Pamulang adalah Pengabdian Kepada Masyarakat yang dilaksanakan setiap semester. Pada semester ganjil tahun ajaran 2022-2023, pengabdian dilaksanakan di SMK Nasional Depok dengan alasan pemilihan pemilihan tersebut karena salah satu SMK swasta yang memiliki fasilitas memadai dengan jumlah siswa yang lumayan banyak namun website yang dimilikinya masih perlu dikembangkan dalam hal inovasi konten yang ada pada websitenya, sehingga kemanfaatannya masih kurang maksimal sebagai media untuk menginformasikan profil, potensi, kegiatan, dan berbagai keunggulan yang dimiliki sekolah kepada masyarakat umum dengan desain grafis yang lebih menarik dan kreatif. Oleh karena itu tim dosen pengabdi mengusulkan suatu workshop atau pelatihan dengan tema Pemanfaatan Aplikasi Canva untuk Desain Grafis bagi Siswa SMK.  Pemilihan aplikasi tidak terlepas dari pertimbangan teknis siswa dan biaya sehingga dipilih aplikasi Canva berbasis web based baik digunakan dengan desktop komputer ataupun smartphone. Pelatihan ini diharapkan mampu menghasilkan siswa yang mampu membuat desain grafis atau konten grafis sederhana sehingga dapat mendukung kegiatan apapun yang positif yang dapat diberdayagunakan secara maksimal.
PENERAPAN NATURAL LANGUAGE PROCESSING ALGORITME SBERT PADA APLIKASI CHATBOT PENERIMAAN MAHASISWA BARU UNIVERSITAS PAMULANG Aji Sakti Ardiansyah; Kahfi Heryandi Suradiradja
Informatika: Jurnal Teknik Informatika dan Multimedia Vol. 6 No. 1 (2026): MEI : JURNAL INFORMATIKA DAN MULTIMEDIA
Publisher : LPPM Politeknik Pratama Kendal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/informatika.v6i1.1645

Abstract

The New Student Admission (PMB) process at Pamulang University currently relies on one-way information channels such as Instagram and brochures only at designated posts. This condition limits access to information for prospective students, especially when they require quick and detailed answers regarding registration requirements, schedules, and tuition fees. Furthermore, the large number of applicants in each admission period makes manual inquiry services inefficient and prone to delays. Therefore, a technology-based solution must provide interactive, fast, and accurate information services. This study aims to develop a Chatbot based on Natural Language Processing (NLP) using the Sentence-BERT (SBERT) algorithm to support the PMB process at the Informatics Engineering Program at Pamulang University. The Chatbot is designed to provide accurate and relevant automated responses to prospective students' inquiries regarding PMB. The SBERT algorithm enhances the Chatbot’s ability to understand sentence meanings and deliver contextual responses. The software development adopts the Waterfall model, encompassing requirements analysis, system design, implementation, and testing phases. The expected outcome is a Chatbot system with- high response accuracy that improves efficiency and accessibility of PMB information services. This research is expected to contribute to the implementation of NLP technology in academic environments.
Analisis Prediksi Permintaan Produk FMCG menggunakan Model LSTM dan DES Untuk Optimalisasi Operasional Gudang Distribusi Tanti Cahya Herdiyani; Taswanda Taryo; Kahfi Heryandi Suradiradja; Zafira Salsabilah
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 7 No 2 (2026): September (In Progress)
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v7i2.213

Abstract

The Fast-Moving Consumer Goods (FMCG) sector is characterized by rapid product turnover and short shelf lives, requiring effective inventory management. Companies such as PT Macrosentra Niagaboga face challenges in maintaining stock availability. Although the inventory system has been integrated, stock verification is still conducted manually, requiring adaptive management to minimize the risk of overstock and stockout. Therefore, this study aims to develop a demand forecasting model using the Long Short-Term Memory (LSTM) algorithm integrated with Discrete-Event Simulation (DES) to optimize inventory management. The study utilized historical shipment data from May 2025 to January 2026. Preprocessing included data cleansing, date validation, and aggregation into daily and weekly data. Forecasting results were subsequently used as inputs for the DES simulations to determine optimal inventory policies. The results demonstrated that weekly LSTM aggregation was more accurate and stable than daily aggregation, as evidenced by a reduction in WAPE from 49.9% to 16.94% for high-demand products. Furthermore, integration with DES reduced stock levels by more than 30% using a safety stock ratio of 0.7 without compromising service levels. Finally, the proposed model was implemented in a web-based dashboard serving as a decision support system for monitoring forecasting and inventory policy recommendations.
Komparasi Decision-Tree dan Naïve Bayes untuk Deteksi Hama dan Penyakit Tanaman Anggur Akhmad Murtado; Kahfi Heryandi Suradiradja
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 2 (2026): Mei-Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i2.12347

Abstract

Kebun Ruang Edukasi Literasi (REL) Rawa Buntu menghadapi kendala dalam mengidentifikasi hama dan penyakit tanaman anggur karena proses deteksi yang masih dilakukan secara manual berdasarkan pengalaman subjektif. Hal ini memicu risiko ketidakakuratan diagnosis yang berdampak pada kegagalan panen. Penelitian ini bertujuan untuk membuat aplikasi deteksi hama dan penyakit tanaman anggur dengan model terbaik dari hasil komparasi antara algoritme Decision Tree dan algoritme Naïve Bayes. Tahapan pada metodelogi penelitian ini meliputi pengumpulan data, pra-proses, pemodelan, evaluasi dan penerapan model. Dataset dari pengumpulan data diambil dari data histori pencatatan terhadap tanaman anggur di Kebun Ruang Edukasi Literasi Rawabuntu. Pemodelan dilakukan menggunakan tool RapidMiner dan evaluasi pengujian model menggunakan confusion matrix. Hasilnya menunjukkan bahwa algoritme Decision Tree menghasilkan performa terbaik dengan tingkat akurasi mencapai 94,12%. Tahap implementasi model dilakukan dengan menerapkan hasil aturan keputusan (rule IF-THEN) yang diekstraksi dari model algoritme Decision Tree pada sebuah aplikasi berbasis web untuk mendeteksi hama dan penyakit pada tanaman anggur di Kebun Ruang Edukasi Literasi Rawabuntu sehingga dapat membantu pengelola kebun dalam mendiagnosis gangguan tanaman anggur secara cepat, tepat, dan akurat. Saran untuk penelitian selanjutnya adalah jumlah dataset yang digunakan lebih banyak dan bervariasi lagi untuk kondisi gejala penyakit anggur sebagai input dari pemodelan.
Implementasi Random Forest Regression untuk Prediksi Kebutuhan Bahan Baku Minyak Senggugu Muhamad Nabukatneja Dwi Kusnandar; Kahfi Heryandi Suradiradja
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 2 (2026): September 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i2.1572

Abstract

CV Tujuh Putik merupakan perusahaan yang memproduksi minyak senggugu dan menghadapi kesulitan dalam merencanakan kebutuhan bahan baku secara akurat karena proses perencanaan masih dilakukan secara manual berdasarkan perkiraan. Kondisi tersebut berpotensi menimbulkan kelebihan maupun kekurangan stok yang dapat mengganggu kelancaran produksi. Penelitian ini bertujuan untuk membangun model prediksi kebutuhan bahan baku minyak senggugu menggunakan tiga algoritme machine learning, yaitu Linear Regression, Support Vector Regression (SVR), dan Random Forest Regression, serta mengimplementasikan Random Forest Regression ke dalam prototipe aplikasi berbasis web. Metodologi penelitian menggunakan tahapan CRISP-DM yang meliputi pemahaman bisnis, pemahaman data, praproses data, pemodelan, evaluasi, dan deployment. Dataset yang digunakan berasal dari data historis operasional CV Tujuh Putik dengan variabel input berupa event e-commerce, kegiatan praktek, produk laku, bulan, dan hari, sedangkan variabel target berupa jumlah bahan baku terpakai. Dataset dibagi menjadi 80% data latih dan 20% data uji, kemudian dievaluasi menggunakan Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), dan R² Score. Hasil pengujian menunjukkan bahwa Support Vector Regression memperoleh nilai MAE dan RMSE terendah, masing-masing sebesar 0,0845 dan 0,1485, sedangkan Linear Regression memperoleh R² Score tertinggi sebesar 0,9958. Random Forest Regression menghasilkan MAE sebesar 0,1345, RMSE sebesar 0,3896, dan R² Score sebesar 0,9907. Berdasarkan fokus penelitian, Random Forest Regression kemudian diimplementasikan sebagai model utama pada prototipe aplikasi berbasis Flask. Aplikasi mampu menghasilkan estimasi kebutuhan bahan baku berdasarkan masukan pengguna. Pengujian sistem dilakukan menggunakan black box dan white box testing untuk memeriksa fungsi serta alur logika aplikasi.
Digital Forensic Analysis of Signature Images Using Error Level Analysis, Image Hashing, and Support Vector Machine Within the DFRWS Framework Amelia Yahya; Taswanda Taryo; Kahfi Heryandi Suradiradja
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7224

Abstract

The increasing use of digital documents in administrative and legal activities has expanded the use of image-based signatures for authentication and verification. However, signature images are vulnerable to manipulation using image-editing software, potentially resulting in document forgery and disputes over authenticity. This study examined the use of Error Level Analysis (ELA), perceptual hashing (pHash), and the Gray Level Co-occurrence Matrix (GLCM) to detect manipulation in signature images. It also evaluated the performance of a Support Vector Machine (SVM) in classifying genuine and forged signatures within the Digital Forensic Research Workshop (DFRWS) framework. The dataset comprised 720 signature images obtained from the Starter Handwritten Signatures Dataset. The research process involved image preprocessing, feature extraction, model training, and performance evaluation using a confusion matrix, accuracy, precision, recall, and F1-score. The model achieved an accuracy of 80.56% on previously unseen test data. The developed system also produced visual analysis outputs and generated digital investigation reports based on the DFRWS framework. These results indicate that the combination of ELA, pHash, GLCM, and SVM can support a structured digital forensic process for distinguishing between genuine and forged signature images.
Pengembangan Model Super Resolution Lightweight Berbasis Hybrid untuk Lingkungan Komputasi Terbatas Ade Soekarno Putra Santoso; Kahfi Heryandi Suradiradja; Choirul Basir
Journal of Creative Student Research Vol. 4 No. 4 (2026): Agustus: Journal of Creative Student Research
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jcsr-politama.v4i4.6537

Abstract

Computational resource limitations, particularly the use of CPU-based systems without GPU acceleration, require super-resolution models that can achieve a balance between reconstruction quality and computational efficiency. This study proposes a lightweight hybrid super-resolution model designed for resource-constrained computing environments. The proposed architecture integrates lightweight feature extraction, residual learning, and an efficient image reconstruction module to enhance image resolution while maintaining low computational complexity. Model performance is evaluated using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), computation time, CPU utilization, and memory consumption under different scaling factors. Experimental results show that the proposed model achieves competitive reconstruction performance while maintaining computational efficiency compared with several lightweight benchmark models. These findings indicate that the proposed hybrid architecture is suitable for deployment in resource-limited computing environments. Future work may focus on evaluating and optimizing the model on edge computing platforms, including smartphones and embedded systems, to validate its performance under real-world operating conditions.
Co-Authors Ade Soekarno Putra Santoso Aji Sakti Ardiansyah Akhmad Murtado Amelia Yahya Choirul Basir Dani Ramdani Muhamad Nabukatneja Dwi Kusnandar Slamet Raharjo Tanti Cahya Herdiyani Taswanda Taryo Zafira Salsabilah