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All Journal Paradikma: Jurnal Pendidikan Matematika JURNAL PENELITIAN SAINTIKA Jurnal IPTEK JURNAL PENGABDIAN KEPADA MASYARAKAT Journal of Tropical Biodiversity and Biotechnology Jurnal KARISMATIKA JTT (Jurnal Teknologi Terpadu) Indonesian Journal of Artificial Intelligence and Data Mining BAREKENG: Jurnal Ilmu Matematika dan Terapan Journal of Information Systems and Informatics bit-Tech Jurnal Sains dan Teknologi JATI (Jurnal Mahasiswa Teknik Informatika) PHI: Jurnal Pendidikan Matematika SKANIKA: Sistem Komputer dan Teknik Informatika Journal of Soft Computing Exploration Djtechno: Jurnal Teknologi Informasi Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) INCODING: Journal of Informatics and Computer Science Engineering Instal : Jurnal Komputer ALACRITY : Journal Of Education Archive: Jurnal Pengabdian Kepada Masyarakat PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Journal of Artificial Intelligence and Engineering Applications (JAIEA) Blend Sains Jurnal Teknik Journal of Student Research EINSTEIN (E-JOURNAL) Ocean Engineering: Jurnal Ilmu Teknik dan Teknologi Maritim Innovative: Journal Of Social Science Research Asian Journal of Applied Education Lencana: Jurnal Inovasi Ilmu Pendidikan Journal of Informatics and Data Science (J-IDS) Journal of Mathematics, Computation and Statistics (JMATHCOS) Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat Journal of Information Technology
Said . Iskandar
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Implementasi Algoritma K-Nearest Neighbors (KNN) dalam Deteksi Dini Hipertensi berdasarkan Analisis Tekanan Darah Ary Prandika Siregar; Said Iskandar Al Idrus; Zulfahmi Indra; Insan Taufik
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.v5i2.1018

Abstract

This study aims to develop a web-based hypertension detection system using the K-Nearest Neighbors (KNN) algorithm and to analyze its accuracy in classifying hypertension status. The dataset was obtained from 447 patient medical records at RSKG Rasyida, consisting of eight variables: gender, age, systolic blood pressure, diastolic blood pressure, height, weight, body mass index (BMI), and hypertension status. The preprocessing stage involved three main steps—feature selection (age, systolic and diastolic blood pressure, BMI), data balancing using undersampling, and data normalization through the Min-Max method—resulting in 425 balanced data samples with five hypertension categories. The web application includes modules for login, dashboard, data input, detection results, and detection history, and has been evaluated using black box testing. The best KNN performance was achieved at k = 13 with 92.94% accuracy, 94% precision, 93% recall, and 93% F1-score. These results indicate that the proposed system can accurately classify hypertension and serve as an effective, data-driven screening tool for healthcare professionals.
Penggunaan K-Means Clustering untuk Segmentasi Tokoh Politik Berdasarkan Potensi Kepemimpinan Di Sumatera Utara M. Ananda Rizki Tambunan; Said Iskandar Al Idrus; Zulfahmi Indra; Yulita Molliq Rangkuti; Sudianto Manullang
ALACRITY : Journal of Education Volume 6 Issue 1 Februari 2026
Publisher : LPPPI Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52121/alacrity.v6i1.986

Abstract

Penelitian ini membahas penerapan algoritma K-Means Clustering untuk mengelompokkan tokoh politik di Sumatera Utara berdasarkan potensi kepemimpinan mereka. Permasalahan yang diangkat meliputi kesenjangan pembangunan antarwilayah, kompleksitas evaluasi kandidat, serta keterbatasan metode penilaian tradisional yang cenderung subjektif. Data yang digunakan merupakan data sekunder dari KPUD Sumatera Utara, Kemendagri, dan media sosial, dengan tiga variabel kuantitatif utama: tingkat pendidikan, pengalaman kepemimpinan, dan tingkat elektabilitas. Proses analisis meliputi normalisasi data, penentuan jumlah klaster optimal menggunakan metode elbow, dan penerapan algoritma K-Means untuk menghasilkan pengelompokan tokoh politik. Hasil penelitian menghasilkan beberapa klaster dengan karakteristik berbeda yang dapat memberikan gambaran profil kepemimpinan potensial di Sumatera Utara. Penelitian ini diharapkan dapat menjadi referensi objektif bagi masyarakat dan pemangku kepentingan dalam pengambilan keputusan politik, sekaligus memberikan kontribusi teoretis terhadap penerapan machine learning dalam analisis politik lokal.
IDENTIFIKASI JENIS PENYAKIT PADA TANAMAN CABAI RAWIT MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) DI DESA BINTANG KECAMATAN SIDIKALANG Todo Josafat Simanjutak; Kana Saputra S; Hermawan Syahputra; Said Iskandar Al Idrus; Didi Febrian
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 1 (2025): JATI Vol. 9 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i1.12403

Abstract

Cabai rawit merupakan jenis tanaman terna atau setengah merdu, memiliki tinggi sekitar 50-120 cm dengan umur bisa mencapai 3 tahun, Prospek cabai rawit cukup menjanjikan untuk memenuhi kebutuhan domestik dan ekspor Namun, produksi justru menurun. Salah satu faktor penyebab rendahnya produksi tanaman cabai adalah adanya gangguan penyakit yang menyerang. Identifikasi penyakit tanaman menjadi langkah penting dalam pemeliharaan dan perawatan, termasuk pada cabai rawit.metode yang digunakan dalam penelitian ini adalah Metode CNN (Convolutional Neural Network) dengan LeNet-5 sebagai arsitekturnya.Penelitian ini berhasil mengembangkan sistem berbasis Convolutional Neural Network (CNN) menggunakan arsitektur LeNet-5 untuk mengidentifikasi dan mengklasifikasi enam kelas penyakit pada tanaman cabai rawit di Desa Bintang, Kecamatan Sidikalang, dengan kinerja yang cukup baik ditunjukkan oleh akurasi 86%, presisi 87%, recall 86%, dan f1-score 86%.Untuk meningkatkan performa sistem, disarankan untuk melakukan eksperimen lebih lanjut dengan mengoptimalkan hyperparameter seperti learning rate dan jumlah epoch, memperluas dataset dengan variasi citra, mengeksplorasi arsitektur model yang lebih modern seperti AlexNet atau ResNet, serta menggunakan perangkat keras dengan spesifikasi yang lebih tinggi untuk efisiensi dan kecepatan pemrosesan yang lebih baik.
Klasifikasi Akun Buzzer Menggunakan Algoritma K-Nearest Neighbor pada Tagar #STYTanpaDiasporaNol di Media Sosial X Afiq Alghazali Lubis; Said Iskandar Al Idrus; Zulfahmi Indra; Kana Saputra S; Chairunisah Chairunisah
Blend Sains Jurnal Teknik Vol. 4 No. 2 (2025): Edisi Oktober
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/blendsains.v4i2.1093

Abstract

Peningkatan pengguna media sosial X pada tahun 2024 sebesar 639 ribu mengakibatkan penyebaran informasi yang sangat masif, menjadikan buzzer berperan dalam mengarahkan opini publik dan memicu konflik sosial, seperti yang terlihat pada tren #STYTanpaDiasporaNol usai gugurnya tim nasional Indonesia di ASEAN Championship 2024. Penelitian ini bertujuan untuk membangun model machine learning dalam klasifikasi akun buzzer menggunakan algoritma K-Nearest Neighbor (KNN). Data yang akan digunakan dalam penelitian ini berasal dari kumpulan tweet dari sosial media X dalam tagar #STYTanpaDiasporaNol. Penelitian ini memiliki prosedur penelitian, di antaranya pengumpulan data, pra-pemrosesan data (cleaning, labelling, feature engineering dan standardization), splitting data, pemrosesan data, dan evaluasi model. Hasil penelitian ini mendapatkan model dengan akurasi terbaik yaitu varian model perbandingan split data 80:20 dan K = 5 dengan nilai akurasi sebesar 89% serta nilai precision dan recall sebesar 89% lalu nilai F1-score sebesar 88%. Model sangat baik dalam memprediksi kelas mayoritas namun kesulitan dalam memprediksi kelas minoritas. Kemudian dilakukan eksperimen resampling data dengan tujuan membuat keseimbangan data. Hasil didapatkan bahwa varian pada split data 70:30 dengan K = 9 diperoleh akurasi sebesar 91% dengan precision, recall dan accuracy juga sebesar 91%. Model eksperimen ini cukup baik mendeteksi kelas mayoritas maupun kelas minoritas.
Implementation of Convolutional Neural Network in Detecting Avocado Ripeness Level Miclyael Luge; Zulfahmi Indra; Hermawan Syahputra; Said Iskandar Al Idrus; Kana Saputra S
Jurnal IPTEK Vol 29, No 1 (2025): May
Publisher : LPPM Institut Teknologi Adhi Tama Surabaya (ITATS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.iptek.2025.v29i1.6737

Abstract

Squeezing avocados to determine ripeness can cause physical damage or bruising, reducing the fruit’s quality and resulting in losses for sellers and buyers. This research aims to develop an Android-based mobile application to detect avocado ripeness based on skin color, avoiding physical damage to the fruit. The study uses three simple Convolutional Neural Network architectures to evaluate the algorithm’s ability to detect avocado ripeness. The dataset includes 385 images across four classes: immature, half-ripe, ripe, and overripe (74 images each), and an additional 89 images for the non-avocado class. The model was trained with learning rates of 0.001, 0.0001, and 0.00001. The architecture with the most convolutional layers achieved the best performance with a 0.001 learning rate, yielding a test accuracy of 94.15%, a test loss of 19.28%, and an F1-score of 94.0%. The best model was then converted to TFLite format and successfully integrated into an Android application that functions effectively.
Classification of Purple Passion Fruit Ripeness Levels Using Convolutional Neural Network (CNN) Mochammad Gani Alfa Alkhoiri Siregar; 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.
ENHANCING LQ45 STOCK PRICE FORECASTING USING LSTM MODEL Marlina Setia Sinaga; Said Iskandar; Sudianto Manullang; Arnita Arnita; Faridawaty Marpaung; Fatizanolo Buulolo
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0427-0438

Abstract

Stocks listed in the LQ45 index represent companies with high liquidity, large market capitalization, and strong fundamentals, making them pivotal to the movements of the Indonesian capital market. This study selects eight LQ45-listed stocks from the energy and mining sectors, as well as the banking sector. Historical data spanning a 10-year period from February 28, 2015, to February 28, 2025. This research aims to mitigate the impact of stock market dynamics, a significant challenge for investor decision-making. The Long Short-Term Memory (LSTM) method was employed to forecast stock prices using four variables: opening, highest, lowest, and closing prices. The LSTM architecture was chosen because its gated memory cells can effectively capture long‑term dependencies and nonlinear patterns in financial time series, thereby aligning with the research objective of minimizing forecasting error under volatile market conditions. Evaluation results using the Mean Absolute Percentage Error (MAPE) showed prediction errors below 2.5%, indicating relatively low forecasting error. Root Mean Squared Error (RMSE) values varied depending on stock price volatility. Companies exhibiting higher stock prices, such as Indo Tambangraya Megah Tbk (ITMG), demonstrate larger RMSE values. For opening prices, predictive accuracy was notably strong, with MAPE values consistently below 1.26%. This suggests that opening prices, influenced by pre-market sentiment and historical data, are more stable and easier to predict compared to other price variables.
INTEGRASI MACHINE LEARNING DAN ANALISIS SPASIAL UNTUK PREDIKSI WILAYAH RAWAN TUBERKULOSIS DI PROVINSI SUMATERA UTARA Fanny Ramadhani; Said Iskandar Al-Idrus; Dian Septiana; Arnita Arnita; Diah Retno Wahyuningrum
Djtechno: Jurnal Teknologi Informasi Vol 6, No 2 (2025): Agustus
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v6i2.6840

Abstract

Tuberkulosis (TBC) masih menjadi masalah kesehatan masyarakat yang serius di Provinsi Sumatera Utara. Prevalensi tinggi terutama di daerah padat penduduk dan terbatasnya akses layanan kesehatan menjadi tantangan utama dalam pengendalian TBC. Penelitian ini bertujuan untuk memprediksi wilayah rawan TBC dengan mengintegrasikan algoritma machine learning dan analisis spasial. Data sekunder diperoleh dari Sistem Informasi Tuberkulosis Nasional (SITB), Badan Pusat Statistik (BPS), dan shapefile administrasi wilayah kabupaten/kota di Sumatera Utara. Variabel prediktor meliputi kepadatan penduduk, status gizi, jumlah fasilitas kesehatan, tingkat kemiskinan, kualitas hunian, dan cakupan imunisasi. Model dikembangkan menggunakan algoritma Random Forest, sementara analisis spasial dilakukan menggunakan QGIS untuk menghasilkan peta risiko TBC. Hasil model menunjukkan akurasi sebesar 86,2% dengan variabel paling berpengaruh adalah kepadatan penduduk, kualitas hunian, dan akses fasilitas kesehatan. Peta risiko yang dihasilkan mengidentifikasi wilayah seperti Kota Medan, Deli Serdang, dan Labuhanbatu sebagai zona merah. Hasil penelitian ini diharapkan menjadi dasar perencanaan intervensi kesehatan yang lebih tepat sasaran di Sumatera Utara.
Identifikasi Penyakit Tanaman Berdasarkan Citra Daun Berbasis Web dengan Pendekatan Algoritma Convolutional Neural Network Sri Mulyana; Mansur AS; Angga Warjaya; Inna Muthmainnah; Said Iskandar Al Idrus; Zulfahmi Indra
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i2.3573

Abstract

This research aims to develop a mustard plant disease classification system using the Convolutional Neural Network (CNN) method integrated into a web-based platform. Classification is carried out on three classes, namely Spotted Mustard Leaves, Rotten Mustard Leaves, Healthy Mustard Leaves, with the addition of the Not Mustard Leaf class as a distractor class to test the robustness of the model against images that are not included in the main classification category. The dataset used consists of 800 images, 200 images each per class. The CNN model was built with a sequential architecture consisting of several convolutions, pooling, dropout, and dense layers, and using ReLU and SoftMax activation functions in the output layer. The training process is carried out up to 100 epochs, but with the use of Early Stopping callback, the training stops at the 60th epoch, with the best performance (best epoch) achieved at the 32nd epoch. Evaluation of the model on test data showed an accuracy of 93.75%, with high precision, recall, and F1-score values in each class. The model was then implemented into a web interface so that users could upload leaf images and obtain classification results automatically. The results of this study show that CNN is effective in detecting mustard leaf disease and has the potential to be applied as a digital image-based diagnostic tool in agriculture.
Application-Driven Parallel Differential Evolution: A Systematic Mapping Review of Scalable Optimization Applications Said Iskandar Al Idrus; Rudi Setiawan
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1695

Abstract

This systematic mapping review examines application-driven parallel Differential Evolution (DE) as a scalable optimization approach for engineering, energy, computing, and intelligent systems. The review is based on a Scopus-only corpus searched on 24 May 2026 using TITLE-ABS-KEY queries related to DE, parallel computing, distributed computing, GPU acceleration, and scalable optimization. From 220 records, 25 unique studies published between 2021 and 2026 were included after screening by year, document type, language, relevance, and methodological alignment. Because the corpus contains heterogeneous benchmark studies, application studies, and hybrid intelligent-system frameworks, the evidence was synthesized thematically rather than through meta-analysis. The synthesis distinguishes explicit parallel-DE implementations from broader scalable DE applications in which scalability is achieved through decomposition, model reformulation, or integration with computationally expensive systems. The findings indicate that GPU acceleration, CUDA, MPI migration, cooperative coevolution, Spark/Hadoop distribution, and resource-aware dispatch frequently report reduced computational cost while preserving or improving solution quality under the evaluated conditions. However, cross-study comparison remains limited by heterogeneous benchmarks, incomplete hardware reporting, inconsistent scalability metrics, and uneven baseline selection. This review contributes a cross-domain taxonomy linking application constraints, DE mechanisms, computational architectures, evaluation metrics, and reported outcomes.
Co-Authors Ada Novisari D. Simanungkalit Adidtya Perdana Afiq Alghazali Lubis Ahmad Landong Alfattah Atalarais Ananda Hatmi, Reza Angga Warjaya Arifin, Khusnul Arnah Ritonga Arnita Arnita Arnita Arnita Arnita Arnita Arnita Arnita Arnita Arnita Ary Prandika Siregar Asiah Asiah Billroy A Ginting Chairunisah Chairunisah Chairunisah Chairunisah, Chairunisah Citra Citra Debi Yandra Niska Dechy Deswita Indriani.S Devi Juliana Napitupulu Diah Retno Wahyuningrum Dian Septiana DIdi Febrian Didi Febrian Eka Nainggolan, Rinay Eko Prasetya, Eko Elvis Napitupulu, Elvis Fadlan Isa Damanik Fadlan Isa Damanik Farhan Ramadhan, Haikal Faridawaty Marpaung Fatizanolo Buulolo Fauziyah Harahap Fira Dilla Fitria, Amanda Hermawan Syahputra Hermawan Syahputra Hermawan Syahputra Ichwanul Muslim Karo Karo Ihsan Zulfahmi Inna Muthmainnah Insan Taufik Izwita Dewi Josua Christian Kana Saputra S Kana Saputra S Kana Saputra S Kana Saputra S Kana Saputra S Kuraini, Atifa Nuzulul Lazuardi Lazuardi M. Ananda Rizki Tambunan M. Revano Ananda Lubis MANSUR AS Manullang, Sudianto Manurung, Jeremia Marlina Setia Sinaga Maulana Malik Fajri Miclyael Luge Miftahul Janna Mika . Layakana Mochammad Gani Alfa Alkhoiri Siregar Molliq Rangkuti, Yulita Mualiawan Firdaus Muhammad Noer Fadlan Muhammad Rifqi Maulana Muthmainnah, Inna Nabila, Rinjani Cyra Nafisa, Anti Nada Nasution, Hamidah . Nerli Khairani Nice R Refisis Niska, Debi Yandra Nurkhalizah, Rezeki Nurliani Manurung Olga Laura Mahlona Pane, M Iqbal Anata Pane, Yeremia Yosefan Puji Prastowo, Puji Purba, Boy Hendrawan Rahmani . . Ramadhani, Fanny Refisis, Nice Rejoice Reza Al Alif Reza Al Alif Rovita Indah Ayu Ningtias Rudi Setiawan Salsabila, Aqila Siburian, Rulli Prasetio Bane Sihombing, Jeremia Jordan Simamora, Elmanani Simanjorang, Rio Givent A Simbolon, Mula Tua Elia Sri Mulyana Sri Mulyana Sudianto Manullang Sudianto Manullang Suryani, Nita Susiana Susiana Susiana Susiana Susiana Syarida Aini, Desti Tarigan, Dewan Dinata Tarigan, Yosua Yosephine Todo Josafat Simanjutak Trisna Utami Putri Wahabi Hasibuan, Rahman Warjaya, Angga Wilma Handayani Yuanita Rachmawati Yulita Molliq Rangkuti Yulita Molliq Rangkuti Yulita Molliq Rangkuti Yusuf, Yusnaeni Zufahmi Indra Zulfahmi Indra Zulfahmi Indra Zulfahmi Indra Zulfahmi Indra, Zulfahmi