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All Journal Jurnal Ilmu Komputer Sistemasi: Jurnal Sistem Informasi Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Inotera Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal Eduscience (JES) JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Jurnal Mandala Pengabdian Masyarakat Jurnal AbdiMas Nusa Mandiri Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology Jurnal Minfo Polgan (JMP) Journal of Renewable Energy, Electrical, and Computer Engineering TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Informasi Dikdimas : Jurnal Pengabdian Kepada Masyarakat Jurnal Kemitraan Responsif untuk Aksi Inovatif dan Pengabdian Masyarakat Jurnal Ilmu Komputer dan Sistem Informasi Journal of Advanced Computer Knowledge and Algorithms Gameology and Multimedia Expert Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS)
Said Fadlan Anshari
Universitas Malikussaleh
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Implementation of ResNet-50 in a Fresh Fruit Bunch (FFB) Ripeness Detection System for Oil Palm M. Rafli Al Thoriq Mustafa; Muhammad Fikry; Said Fadlan Anshari
SISTEMASI Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6331

Abstract

The quality of Crude Palm Oil (CPO) is highly dependent on the accuracy of sorting the ripeness level of oil palm Fresh Fruit Bunches (FFB). Manual sorting processes currently used in factories are vulnerable to human error and subjectivity. This study aims to automate the objectivity of the sorting process using a deep learning model based on the ResNet-50 architecture with a transfer learning approach to classify FFB into three categories: Unripe, Ripe, and Overripe. The computational model was integrated into a web-based application using the Flask framework to support wireless operational use in factories. Experimental results showed a validation accuracy of 90.94% and an F1-score of 91%. Direct field validation using 42 primary data samples achieved a classification success rate of 83.33%. The implementation of a 75% confidence threshold proved effective in preventing prediction errors (zero misclassification), while the Cohen’s Kappa reliability test achieved a score of 0.769, indicating Substantial Agreement with expert evaluators. In conclusion, the ResNet-50-based system demonstrated reliable and objective performance and is considered ready for replication to maintain quality consistency in the palm oil processing industry.
Implementasi Metode Double Exponential Smoothing untuk Prediksi Jumlah Kebutuhan Air di PDAM Tirta Mon Pase Rahmatin Nisak; Arnawan Hasibuan; Said Fadlan Anshari; Rozzi Kesuma Dinata; Fadlisyah Fadlisyah
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 11, No 1 (2026): Januari 2026
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v11i1.5210

Abstract

Clean water is an essential human need, yet its provision is frequently disrupted by demand uncertainty, as experienced by PDAM Tirta Mon Pase with recurring public complaints regarding water supply interruptions. This study aims to design and implement a water demand forecasting system using the Double Exponential Smoothing (Holt’s Linear Trend) method and to evaluate its accuracy. The research utilized monthly historical water production data from January 2022 to December 2024 (36 observations) obtained from PDAM Tirta Mon Pase. The model was applied with smoothing parameters α = 0.8 and β = 0.2, and accuracy was measured using Mean Absolute Percentage Error (MAPE). The results show a very high level of accuracy with an overall MAPE of 3.56% (2022: 4.18%; 2023: 3.91%; 2024: 2.65%), and the forecast predicts water demand in December 2027 will reach 1,131,071.39 m³. It can be concluded that the Double Exponential Smoothing method is highly accurate and effective for forecasting water demand at PDAM Tirta Mon Pase. The developed system is therefore strongly recommended for operational adoption as a strategic decision-support tool in water resource planning, production, and infrastructure development.
Penerapan Metode Naïve Bayes Dalam Sistem Rekomendasi Pemilihan Program Studi Pendidikan Tinggi Berbasis Website Rizki Suwanda; Said Fadlan Anshari; Rizky Putra Fhonna; Tulus Setiawan
Jurnal Minfo Polgan Vol. 14 No. 2 (2025): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v14i2.15858

Abstract

Pemilihan program studi pendidikan tinggi merupakan keputusan penting yang berdampak pada arah karir dan pengembangan potensi siswa. Namun, banyak siswa mengalami kesulitan dalam menentukan pilihan yang sesuai dengan minat dan kemampuan akademik mereka. Dalam praktiknya, pemilihan program studi masih sering dilakukan secara subjektif tanpa dukungan data atau sistem yang dapat membantu proses pengambilan keputusan secara rasional dan terukur. Penelitian ini bertujuan untuk mengembangkan sebuah sistem rekomendasi berbasis metode Naïve Bayes yang mampu memberikan saran program studi kepada siswa berdasarkan data minat dan prestasi akademik. Metode Naïve Bayes dipilih karena mampu mengklasifikasikan data secara efisien dengan pendekatan probabilistik, meskipun asumsi antar atribut bersifat independen. Sistem ini diharapkan dapat menjadi alat bantu bagi siswa maupun pihak sekolah (seperti guru BK) dalam memberikan arahan akademik berbasis data. Tahapan penelitian dimulai dari studi literatur dan perancangan sistem, dilanjutkan dengan pengumpulan data berupa minat siswa dan nilai akademik, baik melalui dataset simulasi maupun data uji terbatas dari responden nyata. Data tersebut kemudian diproses dan digunakan untuk membangun model klasifikasi menggunakan algoritma Naïve Bayes. Selanjutnya, sistem diuji untuk mengukur akurasi dan efektivitasnya dalam memberikan rekomendasi program studi yang sesuai. Penelitian ini juga mencakup evaluasi sistem berdasarkan hasil klasifikasi serta analisis keterkaitan antara input (minat dan prestasi) dan output rekomendasi program studi.
IoT-Based LPG Gas Leak Monitoring System with Automatic Alarms Muhammad Hafizal; Safwandi Safwandi; Kurniawati Kurniawati; Muchlis Abd Muthalib; Said Fadlan Anshari
SISTEMASI Vol 15, No 6 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i6.6348

Abstract

Liquefied Petroleum Gas (LPG) leaks are one of the leading causes of fires in households and LPG distribution facilities. Limited public awareness and delays in detecting gas leaks can significantly increase the risk of property damage and endanger human safety. Therefore, this study aims to design and develop an Internet of Things (IoT)-based monitoring system for 3 kg LPG gas leaks that provides automatic, real-time early warning notifications. The proposed system employs an MQ-2 gas sensor to detect LPG concentration, a NodeMCU ESP8266 as the main controller and data communication module, and a buzzer, LED, and LCD as local warning indicators. A threshold-based method is implemented to classify safe and hazardous conditions according to predefined gas concentration limits. In addition, the system is integrated with remote notifications via Telegram, enabling users to receive alerts even when they are away from the monitored location. Experimental results demonstrate that the proposed system can accurately detect LPG gas leaks under various operating conditions and automatically send warning notifications whenever the gas concentration exceeds the predefined threshold. These findings indicate that the system is effective, responsive, and has strong potential to enhance user safety in the use of LPG.
Gerakan EISGU (Ethical and Islamic Gadget Use) sebagai Solusi Pendidikan dalam Menghadapi Tantangan Jam Malam bagi Anak-Anak di Lhokseumawe Zainul Mujtahid; Muh Fahrudin Alawi; Said Fadlan Anshari; Islami Fatwa
Jurnal Mandala Pengabdian Masyarakat Vol. 7 No. 1 (2026): Jurnal Mandala Pengabdian Masyarakat
Publisher : Progran Studi Farmasi Universitas Mandala Waluya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35311/jmpm.v7i1.828

Abstract

The EISGU Movement (Ethical and Islamic Gadget Use) was implemented as a community service initiative to address the growing issue of excessive gadget use among adolescents during the night curfew policy in Blang Pulo Village, Lhokseumawe. The program aimed to foster ethical, Islamic, and productive digital behavior through an integrative educational approach combining religious values, character formation, and digital literacy. Conducted at SMPN 8 Lhokseumawe with 25 student participants, the program consisted of four main components: (1) Digital Ethics Education Based on Islamic Principles, (2) Educational Digital Content Creation Workshops using AI tools such as Gemini AI and ChatGPT, (3) Basic Programming (Coding) Workshops using Google Colab, and (4) the EISGU Declaration. Program effectiveness was evaluated by teacher observers using a rubric with seven indicators rated on a 4-point scale, yielding an overall effectiveness score of 3.8 out of 4, which indicates a very high level of achievement across ethical understanding, engagement, creativity, and responsible gadget use. These findings demonstrate that integrating Islamic values with digital literacy and AI-assisted learning enhances adolescents’ moral awareness, self-control, and 21st-century technological competencies, positioning the EISGU Movement as an innovative, faith-based educational model with strong potential for replication in other communities facing similar digital challenges.
SISTEM MONITORING DAN PENDETEKSI PENCEMARAN UDARA SEKOLAH BERBASIS INTERNET OF THINGS : INTERNET OF THINGS BASED SCHOOL AIR POLLUTION MONITORING AND DETECTION SYSTEM Aryo Wibisono Putra; Zahratul Fitri; Said Fadlan Anshari
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6347

Abstract

SMK Negeri 3 Lhokseumawe is located in a high activity area surrounded by hotels, workshops and residential areas. Based on internal school data, student complaints related to respiratory problems increased significantly from 8% in 2020 to 30% in 2024. This condition shows the urgency of the need for an air quality monitoring system that is adaptive, efficient, and can operate in real-time in the school environment. This research aims to develop an Internet of Things (IoT)-based air pollution monitoring and early detection system with the integration of Takagi Sugeno Kang (TSK) fuzzy method. The system uses an ESP32 microcontroller connected to MQ-135 (CO₂), MQ-7 (CO), GP2Y1010AU0F (PM10), and DHT22 (temperature and humidity) sensors. In contrast to conventional approaches that only read raw data or rely on fixed thresholds, the TSK fuzzy method is able to adaptively process multivariate data and produce more precise air quality classifications. Data is sent in real-time to the server and displayed via website and LCD. Tests were conducted for 7 hours with 100 samples under relatively controlled environmental conditions. The implementation results show that the system runs stably and accurately, one of which is the measurement of CO 8.26 ppm, CO₂ 587 ppm, PM10 36.34 µg/m³, temperature 29.60°C, and humidity 76.50%, which is classified as “Fair” based on a fuzzy value of 2.303735. This research fills the literature gap by optimizing the TSK fuzzy method on a resource-limited device (ESP32), and offers novelty in the presentation of air quality information quickly and contextually to support health risk mitigation in educational environments.
PENERAPAN DECISION TREE C5.0 DALAM APLIKASI ANALISIS SENTIMEN TERHADAP BOIKOT PRODUK PRO-ISRAEL DI MEDIA SOSIAL X: SENTIMENT CLASSIFICATION ON BOYCOTT-RELATED TWEETS USING C5.0 DECISION TREE ALGORITHM Juliar Husriansyah; Asrianda Asrianda; Said Fadlan Anshari
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6451

Abstract

The movement to boycott products believed to support Israel reflects global solidarity with the Palestinian fight. In Indonesia, support for this movement continues to grow, especially through social media platform X (formerly Twitter). After the release of MUI Fatwa Number 83 of 2023, which advises Muslims to refrain from using products linked to Israel. The objective of this study is to analyze the sentiment of users on social media X regarding the boycott of products that support Israel, using the Decision Tree C5.0 algorithm. The data were collected through a scraping technique targeting tweets containing relevant boycott-related keywords, then processed using text preprocessing and analyzed using Term Frequency-Inverse Document Frequency (TF-IDF) for the extraction of features. The dataset was divided into 80% for training and 20% for testing in order to train and assess the classification model. The classification results revealed that out of 1,840 tweets, 1,257 were positive, 318 negative, and 265 neutral, indicating that 68.32% of users expressed support for the boycott movement. The evaluation of the model resulted in an accuracy of 83.26%, a precision of 86.51%, a recall of 83.26%, and an f1-score of 84.29%, demonstrating that the C5.0 algorithm effectively and accurately classifies sentiment. This research is anticipated to act as a guide for creating systems that analyze public opinion and provide insights for policymakers and industry players in responding to social issues emerging on digital platforms.
PENERAPAN PERCEIVED STRESS SCALE DALAM PSYCHOLOGICAL SELF-ASSESSMENT UNTUK MENGUKUR TINGKAT STRES MENGGUNAKAN METODE K-NEAREST NEIGHBORS Nadia Rizatul Riski; Safwandi Safwandi; Said Fadlan Anshari
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6479

Abstract

Stress is a common psychological issue experienced by university students, particularly in high-pressure academic environments such as engineering faculties. This study aims to develop a digital self-assessment system to measure student stress levels using the Perceived Stress Scale (PSS-10) and the K-Nearest Neighbors (KNN) classification method. Data were collected from 100 respondents from the Faculty of Engineering at Universitas Malikussaleh. The system classifies stress levels into three categories: mild, moderate, and severe. Testing was conducted using 5-fold cross-validation. The evaluation results show an average accuracy of 83%, weighted precision of 79.18%, weighted recall of 84.70%, and weighted F1-score of 80.10%. These findings indicate that the system is capable of providing fairly accurate stress classification and can serve as a useful tool for independent stress detection.
IMPLEMENTASI AUGMENTED REALITY UNTUK PENGENALAN TANAMAN TOGA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK Melita Saldila; Rozzi Kesuma Dinata; Said Fadlan Anshari
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.6602

Abstract

This research aims to develop an Augmented Reality (AR) based application integrated with Convolutional Neural Network (CNN) method to help communities recognize Family Medicinal Plants (TOGA) interactively and increase awareness of their potential benefits. The developed application uses AR technology to provide direct information about TOGA plants detected through mobile phone cameras, with a dataset covering 10 types of TOGA plants, each containing 200 images per label. The research results show that the system successfully performs plant recognition in real-time with an accuracy rate of 58.53%, precision of 58.76%, and recall of 99.40%. The CNN model is capable of recognizing various visual variations of plants under different lighting conditions and viewing angles. Model training was conducted up to 125,000 steps with the best performance achieved at the 72,000th checkpoint. Although the application can provide an engaging and effective learning experience, the main challenge faced is the diversity of physical forms of plants within each category that affects system accuracy. This research proves that the combination of AR and CNN technologies can be used as an innovative solution for medicinal plant education, although further development is still needed to improve recognition accuracy.
IMPLEMENTASI METODE CONTENT-BASED FILTERING DALAM REKOMENDASI KEDAI KOPI DI KOTA LHOKSEUMAWE Muhammad Arrayyan; Rozzi Kesuma Dinata; Said Fadlan Anshari; Fadlisyah; Maryana
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.6641

Abstract

Lhokseumawe a city known for its numerous coffee shops, serves as the focus of this study, which aims to develop a coffee shop recommendation system using a content-based filtering approach based on Google Maps review analysis. A total of 54 coffee shops were collected through web scraping and filtered to 32, as only these shops provided sufficient and relevant reviews according to the selected keywords. User reviews were processed through preprocessing, TF-IDF weighting, and cosine similarity to measure the alignment between user preferences and shop characteristics. A scenario-based evaluation was conducted by using keywords such as “noodles,” “parking,” “spacious,” “toilet,” and “watching together” to represent user preferences. The results show that the system generates recommendations consistent with the presence and relevance of these keywords, with shops such as AN Coffee and Arabica Kopi frequently appearing as top suggestions. Although the evaluation is limited to scenario-based testing, the system demonstrates potential in assisting users in selecting suitable coffee shops. Future work may include hybrid filtering, machine learning methods, automated keyword extraction through topic modeling, and user-based evaluation to improve recommendation quality.
Co-Authors Agus Maula Rizki Al-Ghiyats, Said Ananda, Ria Angkasa, M. Ishlah Buana Arnawan Hasibuan Aryo Wibisono Putra Asrillah Asrillah Bustami Bustami Chan, Fajar Ricaldi Dahlan Abdullah Dani Ramdani Dinda, Dinda Fadlisyah Fadlisyah Fadlisyah Fahrezi, Irgi Fanita, Fanita Hidayatsyah Hidayatsyah Islami Fatwa Juliar Husriansyah Juliawati Khairi, Abil Kurniawati Kurniawati Lidya Rosnita Limbong, Hendra Putranta M Ishlah Buana Angkasa M. Rafli Al Thoriq Mustafa Maryana Maryana, Maryana Maulana, O.K. Muhammad Majid Melita Saldila Mhd Firza Ryzaaldy Muchlis Abdul Muthalib Muh Fahrudin Alawi Muhammad Arrayyan Muhammad Daud Muhammad Fikry Muhammad Hafizal Mukti Qamal Mukti Qamal Munirul Ula Nadia Rizatul Riski Nur Fazri Husna Pohan, Fadlyani Putri Agustina Dewi Putri, Nazirah Allisya Rahmatin Nisak Rini Meiyanti Rizal Rizal Rizal Rizal Rizal S.Si., M.IT, Rizal Rizal, Reyhan Achmad Rizki Suwanda Rizki, Sukma Rizky Putra Fhonna Rozzi Kesuma Dinata Safwandi Safwandi Sahputra, Irfan Samudera, Brucel Duta Sandy, Cut Lika Mestika Sinaga, Rifky Fahriza Sudirman Sudirman Sujacka Retno Sukiman, T. Sukma Achriadi Sukma Rizki Syahriani Putri Ayu Syibral Malasyi, Syibral Tiara Sartika Tri Ramdhany Tulus Setiawan Wardina Ningsih Yasin, Fijri Ahmad Zahratul Fitri Zainul Mujtahid Zalfie Ardian Zara Yunizar Zarayunizar Zarayunizar