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All Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Journal of Applied Engineering and Technological Science (JAETS) Jurnal Sistem Komputer dan Informatika (JSON) Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) ABDI SABHA (Jurnal Pengabdian kepada Masyarakat) Journal of Social Responsibility Projects by Higher Education Forum Jurnal ABDIMAS Budi Darma TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Jurnal Sains Student Research Jurnal Komprehenshif Indonesian Journal of Multidisciplinary Scientific Studies Jurnal Sains, Teknologi dan Komputer
Fatma Sari Hutagalung
Program Studi Magister Teknik Informatika Fakultas Ilmu Komputer Dan Teknologi Informasi Universitas Sumatera Utara
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Visual Detection of Oil Palm Maturity Leveraging Simple Evolving Connectionist System Al-Khowarizmi Al-Khowarizmi; Fatma Sari Hutagalung; Halim Maulana
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/kvqm6450

Abstract

Detecting the ripeness of oil palm fruit bunches is a crucial process in the palm oil industry to ensure the quality and quantity of oil extracted. Conventional methods still rely on subjective and inefficient manual observation. This study proposes a visual detection system using the Simple Evolving Connectionist System (SECoS) algorithm to identify the ripeness of oil palm bunches based on visual images. This model utilizes color, texture, and shape characteristics extracted from images and processed through an adaptive and evolving neural network structure. The results demonstrate that SECoS is capable of high detection accuracy and adapts to new data patterns. This system has the potential to be applied in precision agriculture practices. The model achieved an average accuracy of 91.3%, with the highest accuracy of 94% in the "Ripe" category in the final test based on 300 dataset. This demonstrates that parameter optimization is crucial in improving the model's ability to adapt to variations in oil palm bunch image data. Accuracy improvements were evident in both training and validation data. However, not all categories achieved optimal results, with accuracy for the "empty bunch" labels (89%) and "unripe" labels (88%) being relatively lower than for the other categories.
Intervensi Terfase Model IN-ON-IN untuk Mengatasi Gap Implementasi Literasi Koding dan AI Guru SD di Sumatera Utara Akhyar Lubis; Fatma Sari Hutagalung; Riah Ukur Ginting; Mesran Mesran; Juanda Hakim Lubis; Fajrul Malik Aminullah Napitupulu
Journal of Social Responsibility Projects by Higher Education Forum Vol 6 No 3 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jrespro.v6i3.9752

Abstract

This community service program aimed to address the gap between conceptual knowledge and practical implementation ability of elementary school (SD) teachers in coding and artificial intelligence (AI) instruction in Medan City and Deli Serdang Regency. Initial needs analysis revealed that while many teachers possessed basic familiarity with coding and AI terminology, the majority had not independently designed lesson plans incorporating coding activities nor systematically used coding platforms in classroom settings. Partner schools also faced uneven device availability and internet connectivity, alongside weak post-training support structures that hindered the transfer of learning to classroom practice. The intervention applied the IN-ON-IN model with contextual coaching: In Service Training 1 (conceptual and practical workshops using Scratch 3.0, Code.org, and Teachable Machine), On-the-Job Training (classroom implementation with field coaching and telementoring), and In Service Training 2 (reflection and consolidation). The program involved 175 elementary school teachers from both regions, implemented October 2025 through February 2026. Evaluation used an explanatory sequential mixed-methods design: pretest–posttest, classroom observation rubrics, learning artifact analysis, and in-depth interviews and focus groups. Results showed knowledge score improvement from 81% to 95% with reduced standard deviation (3.11 to 1.09), alongside qualitative findings confirming gains in lesson plan design competency and project-based coding instruction for the majority of participants. Persistent barriers included limited devices, intermittent internet access, and time constraints for lesson planning. Recommendations emphasize low-resource module development, expansion of telementoring, and cross-stakeholder collaboration to strengthen school infrastructure.
Implementasi Sistem Pendukung Keputusan Berbasis Internet of Things (IoT) Untuk Menentukan Kesehatan Tanaman Bunga Kertas Menggunakan Metode Simple Additive Weighting (SAW) Asmaul Husna; Fatma Sari Hutagalung
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 2 (SEMNASTIK) (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akun
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No2(SEMNASTIK).pp31-39

Abstract

Ornamental plants are plants that are in great demand these days because of their beauty and attractiveness. Ornamental plants are an important aspect because they are able to maintain environmental health, the more ornamental plants the better it is also to beautify the environment. In this research, a determination process is carried out to determine the health of ornamental plants, one of which is Bougenville Flower or commonly referred to as paper flower, which is an ornamental plant whose existence is quite popular among the public and is widely spread in various regions in Indonesia. The method used in this research is the Simple Additive Weighting (SAW) method to rank alternatives. The criteria used are 3 namely air temperature, air humidity, and soil pH, while the alternatives used are 10 paper flower ornamental plants with the aim of ranking the best or healthy paper flower plants from the samples tested. Implementation with the SAW method produces recommendations for paper flower ornamental plants, namely A3 or paper flower 3 accompanied by the results of ranking values with a value of 0.992 which is the highest value compared to other alternatives. This research has also been calculated using Confusion Matrix with an accuracy test result of 60%.
Analisis dan Implementasi Metode Profile Matching dalam Mendiagnosis Kesehatan Mental Siswa Liona Hasibuan; Fatma Sari Hutagalung
Indonesian Journal of Multidisciplinary Scientific Studies Vol 4 No 4 (2026): Edisi Juni-Juli 2026
Publisher : Lembaga Penelitian Dan Pengabdian Masyarakat (LPPM) STAI Raudhatul Akmal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33151/ijomss.v4i4.916

Abstract

Stres pada remaja merupakan permasalahan yang semakin kompleks di lingkungan sekolah, namun metode identifikasi yang digunakan selama ini masih bersifat konvensional dan subjektif. Penelitian ini bertujuan untuk menganalisis dan mengimplementasikan metode Profile Matching dalam mendiagnosis tingkat stres pada remaja di SMA Negeri 1 Portibi, serta membangun sistem informasi berbasis web sebagai alat bantu bagi guru Bimbingan Konseling (BK). Pengumpulan data dilakukan melalui observasi dan wawancara langsung dengan Kepala Bagian BK, menghasilkan delapan kriteria diagnosis stres yang mencakup frekuensi overthinking, gangguan tidur, kehilangan minat belajar, gejala fisik akibat stres, perubahan emosi, interaksi sosial, kecenderungan perfeksionisme, serta keseimbangan akademik dan nonakademik. Data dari 12 siswa dikumpulkan melalui kuesioner berskala Likert dan dianalisis menggunakan metode Profile Matching dengan kriteria dibagi menjadi core factor (60%) dan secondary factor (40%). Hasil analisis menunjukkan bahwa 2 siswa (16,7%) berada pada kategori stres rendah, 4 siswa (33,3%) stres sedang, dan 6 siswa (50%) stres tinggi. Pengujian sistem dilakukan dengan metode Black Box Testing dan seluruh fungsi sistem berjalan dengan valid. Penelitian ini menyimpulkan bahwa metode Profile Matching efektif diterapkan dalam diagnosis stres remaja secara objektif, konsisten, dan terstruktur.
Analisis Preferensi Konsumen dalam Pembelian Laptop Berdasarkan Kebutuhan Pengguna di Tokopedia Menggunakan Algoritma Fuzzy SVM (Support Vector Machine) Mutia Sari; Fatma Sari Hutagalung
Indonesian Journal of Multidisciplinary Scientific Studies Vol 4 No 4 (2026): Edisi Juni-Juli 2026
Publisher : Lembaga Penelitian Dan Pengabdian Masyarakat (LPPM) STAI Raudhatul Akmal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33151/ijomss.v4i4.917

Abstract

Perkembangan teknologi informasi mendorong pertumbuhan pesat e-commerce, termasuk penjualan laptop melalui platform Tokopedia. Banyaknya pilihan merek, spesifikasi, dan harga seringkali menyulitkan konsumen menentukan laptop yang paling sesuai kebutuhan. Penelitian ini bertujuan mengembangkan sistem rekomendasi pemilihan laptop menggunakan metode Fuzzy Support Vector Machine (Fuzzy SVM). Metode ini menggabungkan kemampuan klasifikasi SVM dengan logika fuzzy untuk menangani ketidakpastian dan ambiguitas dalam data preferensi konsumen. Dataset yang digunakan terdiri dari 1.000 data produk laptop dari Tokopedia dengan atribut harga, RAM, SSD, dan rating. Setelah fuzzifikasi dan normalisasi, model menggunakan kernel Radial Basis Function (RBF) dengan fungsi skor berbobot: Score = (Harga×0,4) + (RAM×0,3) + (SSD×0,3), dengan ambang batas rekomendasi sebesar 0,25. Hasil pengujian menunjukkan sistem mampu mengklasifikasikan preferensi pembelian laptop secara konsisten. Laptop dengan nilai score tertinggi (0,44) adalah HP EliteBook 14 Gen 4 (RAM 64 GB, SSD 1024 GB, harga Rp 3.560.460). Kombinasi metode Fuzzy dan SVM terbukti efektif menganalisis preferensi konsumen di platform e-commerce.
Pengembangan Prototype Sistem Pintu Otomatis Berbasis Internet Of Things (IoT) Dengan Kendali Smartphone Dan Penguncian Otomatis Untuk Meningkatkan Kemandirian Penyandang Disabilitas Fauzan Habib; Fatma Sari Hutagalung
JOURNAL SAINS STUDENT RESEARCH Vol. 4 No. 5 (2026): Oktober: Jurnal Sains Student Research
Publisher : CV. KAMPUS AKADEMIK PUBLISING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jssr.v4i5.12732

Abstract

Penelitian ini bertujuan untuk mengembangkan dan menguji prototype sistem pintu otomatis berbasis Internet of Things (IoT) dengan kendali smartphone dan penguncian otomatis. Penelitian menggunakan metode Research and Development (R&D) untuk menghasilkan prototype yang mengintegrasikan perangkat keras dan perangkat lunak. Sistem menggunakan ESP32 NodeMCU sebagai pusat pengendalian yang terhubung dengan Arduino IoT Cloud melalui jaringan Wi-Fi. Prototype terdiri atas sensor ultrasonik HC-SR04, servo motor MG90S, relay 1 channel, solenoid door lock 12V, LCD 16×2 I2C, buzzer, Step Down LM2596, dan adaptor 12V 3A. Sistem memiliki dua mode pengoperasian melalui dashboard smartphone, yaitu Buka Pintu dan Buka Pintu Tahan. Pada mode Buka Pintu, sistem membuka kunci melalui solenoid dan menggerakkan servo untuk membuka pintu, kemudian menggunakan perubahan kondisi yang dideteksi sensor HC-SR04 sebagai bagian dari logika penutupan otomatis. Sementara itu, mode Buka Pintu Tahan mempertahankan pintu dalam kondisi terbuka selama perintah masih aktif dan menjalankan proses penutupan setelah perintah dinonaktifkan. Pengujian dilakukan secara fungsional untuk mengetahui kemampuan setiap komponen dan keseluruhan sistem dalam menjalankan fungsi sesuai rancangan. Hasil pengujian menunjukkan bahwa ESP32 dapat menerima perintah melalui Arduino IoT Cloud, servo dapat menggerakkan pintu dari posisi 5° menuju 110° dan kembali ke posisi awal, relay dan solenoid dapat menjalankan fungsi pelepasan serta penguncian, sensor HC-SR04 dapat mendeteksi perubahan kondisi objek, LCD dan buzzer dapat memberikan informasi dan indikator sesuai program, serta kedua mode pengoperasian dapat berjalan sesuai rancangan. Secara keseluruhan, prototype yang dikembangkan dapat menjalankan fungsi utama sistem pintu otomatis melalui kendali smartphone dan penguncian otomatis sesuai dengan rancangan penelitian.
Pengembangan sistem pertanian cerdas berbasis IOT untuk deteksi dini hama pada tanaman padi Muhammad Irfan; Fatma Sari Hutagalung
Jurnal Sains, Teknologi & Komputer Vol. 2 No. 3 (2025): Jurnal Sains, Teknologi & Komputer (SAINTEK)
Publisher : Lembaga Riset Mutiara Akbar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/saintek.v2i3.1284

Abstract

Golden apple snails represent a critical threat that drastically diminishes rice crop productivity. This study aims to develop an Internet of Things (IoT)-based smart farming system for the early detection of golden apple snail infestations in paddy fields. The system employs an external Logitech camera integrated with the YOLOv8 object detection model, supported by DHT22, soil moisture, and LDR sensors to monitor microclimate variables in real time. Applying the Research and Development (R&D) framework, field testing was conducted over three consecutive days in the rice fields of Dolok Masihul. System evaluation involved direct field observation, functional black-box assessments, and user perception surveys using the Technology Acceptance Model (TAM). The results demonstrate that the system successfully identifies golden apple snails accurately across varying ambient lighting conditions and attains an overall user acceptance rate of 90.63%.
Deep learning-based object detection application for medical images using the vision transformer (ViT) method Ade Rinanda Wahyuni Hasibuan; Fatma Sari Hutagalung
Jurnal Sains, Teknologi & Komputer Vol. 3 No. 3 (2026): Jurnal Sains, Teknologi & Komputer (SAINTEK)
Publisher : Lembaga Riset Mutiara Akbar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/saintek.v3i3.1322

Abstract

Ischemic stroke is a medical condition that requires rapid and accurate diagnosis because it can cause permanent brain damage. Medical images, such as CT scans, can serve as a source of information to assist healthcare professionals in identifying patients' brain conditions. However, manual image analysis is time-consuming and may be affected by human limitations in image interpretation. This study aims to develop a CT scan image classification application to assist in identifying ischemic stroke using the Vision Transformer (ViT) method. The research stages include data preprocessing, patch embedding, positional encoding, transformer encoder, and classification head to classify images into two categories: stroke and normal. The dataset consists of patient CT scan images obtained from Hendra Hospital, Binjai. The system was implemented using the Python programming language and the FastAPI framework. The evaluation results show that the ViT model achieved an accuracy of 82.93% in the final testing. These results indicate that the ViT model can be used to classify CT scan images into stroke and normal categories on the dataset used in this study. This research may serve as an approach for developing deep learning-based decision support systems to assist in the analysis of medical images.
SISTEM ABSENSI BERBASIS IOT UNTUK MENGUKUR TINGKAT STRES SISWA MENGGUNAKAN ALGORITMA DECISION TREE Muhammad Rakha Alfajar; Fatma Sari Hutagalung
Komprehensif Vol 4 No 2 (2026)
Publisher : CV Edu Tech Jaya

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

Abstract

Penelitian ini bertujuan untuk merancang dan membangun sistem absensi siswa berbasis Internet of Things (IoT) yang terintegrasi dengan pengukuran tingkat stres secara real-time menggunakan algoritma Decision Tree. Latar belakang penelitian ini didasari oleh kelemahan sistem absensi konvensional yang masih rentan terhadap manipulasi data serta belum adanya integrasi dengan pemantauan kondisi kesehatan siswa.Sistem yang dikembangkan memanfaatkan teknologi biometrik berupa sensor fingerprint sebagai media identifikasi kehadiran siswa, serta sensor detak jantung dan sensor suhu tubuh (MLX90614) untuk memperoleh data fisiologis yang digunakan sebagai indikator tingkat stres. Data yang diperoleh kemudian diproses menggunakan algoritma Decision Tree untuk mengklasifikasikan kondisi siswa ke dalam kategori normal, stres sedang, atau stres tinggi. Seluruh data dikirimkan secara real-time melalui jaringan internet menggunakan mikrokontroler ESP32 dan ditampilkan pada aplikasi monitoring berbasis web.Metode penelitian yang digunakan adalah Research and Development (R&D) yang meliputi tahap analisis kebutuhan, perancangan sistem, pengembangan prototype, serta pengujian dan evaluasi sistem. Hasil penelitian menunjukkan bahwa sistem mampu melakukan identifikasi kehadiran secara akurat, membaca data detak jantung dan suhu tubuh dengan baik, serta mengirimkan informasi secara real-time ke sistem monitoring. Selain itu, algoritma Decision Tree dapat digunakan secara efektif dalam mengklasifikasikan tingkat stres siswa berdasarkan parameter fisiologis.Dengan adanya sistem ini, diharapkan dapat meningkatkan efisiensi dan akurasi absensi siswa sekaligus memberikan nilai tambah berupa pemantauan kondisi kesehatan secara objektif. Sistem ini juga dapat membantu pihak sekolah dan orang tua dalam memonitor kondisi siswa secara berkelanjutan serta mendukung pengambilan keputusan yang lebih cepat dan tepat.
Co-Authors Ade Rinanda Wahyuni Hasibuan Akhyar Lubis Al Khowarizmi Asmaul Husna Budi Kurniawan Hutasuhut Efendi, Syahril Fajrul Malik Aminullah Napitupulu Farhan Al-iksan Al-iksan Farid Akbar Siregar Fauzan Habib Ferdy Riza Halim Maulana Herman Mawengkang Indah Purnama Sari Indah Purnama Sari Juanda Hakim Lubis Liona Hasibuan Martiano, Martiano Mesran Mesran Mhd. Basri Muhammad Irfan Muhammad Rakha Alfajar Mutia Sari Ramadhani, Fanny Riah Ukur Ginting Yoshida Sary