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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 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 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
Universitas Negeri Medan
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Google Site Untuk Pembelajaran Online Said Iskandar Al Idurs; Zufahmi Indra; Mualiawan Firdaus; Arnita
Darma Abdi Karya Vol. 2 No. 2 (2023): Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat
Publisher : LPPM POLITEKNIK LP3I

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/darmaabdikarya.v2i2.1710

Abstract

Pembelajaran di era pandemi covid-19 membuat guru memaksimalkan penggunaan media pembelajaran berbasis Information and Communication Technology(ICT). Untuk itu dibutuhkan berbagai upaya dalam pembelajaran termasuk penggunaan media pembelajaran berbasis ICT agar memudahkan guru dalam proses pembelajaran. Pentingnya peningkatan kompetensi dan keterampilan guru dalam memanfaatkan dan memaksimalkan media pembelajaran berbasis ICT sehingga perlu adanya pelatihan dalam membuat dan menggunakan media pembelajaran berbasis ICT kepada guru. Pelatihan pembuatan dan penggunaan google sites sebagai media pembelajaran dilaksanakan pada tanggal 9 Agustus 2022 Pelatihan ini bertujuan untuk meningkatkan kompetensi guru melalui penggunaan Google Sites sebagai media pembelajaran online. Pelaksanaan pelatihan ini terdiri atas 2 tahapan yaitu pertama penyampaian materi mengenai pembuatan dan penggunaan google sites sebagai media pembelajaran dan kedua praktek langsung membuat dan menggunakan google sites sebagai media pembelajaran online. Metode dalam pelatihan ini adalah ceramah, tanya jawab dan eksperimen. Pelatihan ini diikuti dengan antusiasme dari peserta yang diikuti oleh 30 orang guru SMP NEGERI 3 PANCUR BATU. Pelatihan ini menambah pengetahuan dan kompetensi serta skill peserta dalam membuat dan menggunakan google sites sebagai media pembelajaran.. Hasil dari angket yang disebarkan kepada para guru setelah kegiatan ini berakhir menunjukkan bahwa kegiatan ini sangat bermanfaat dan mereka berharap kegiatan yang semacam ini diadakan lagi pada tahun yang akan datang.
Sistem Pakar Diagnosa Penyakit Kasat Mata Pada Sapi Berbasis Android Reza Al Alif; Said Iskandar Al Idrus
Journal of Student Research Vol. 1 No. 2 (2023): Maret : Journal of Student Research
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jsr.v1i2.1062

Abstract

Sapi merupakan hewan yang hidup di darat, yang menjadikan salah satu dari sekian banyak sumber protein dan lemak yang dibutuhkan manusia. Sapi merupakan hewan pemakan tumbuhan yang sangat berguna bagi banyak orang terutama dari segi susu, daging, kulit dan kotorannya. Komoditas telur, daging, dan susu ialah komoditas pangan yang memiliki protein dan lemak yang tinggi. Sapi juga termasuk dalam kategori hewan ternak. Dalam menjaga kualitas serta pemeliharaan sapi ditemukannya kendala, yakni adanya penyakit yang menyerang sapi. Hal ini menjadi hambatan bagi peternak sapi. Kendala dalam mendiagnosis penyakit sapi ialah kurangnya pengetahuan peternak tentang penyakit sapi, keterbatasan waktu, dan pengambilan keputusan dalam proses pencegahan. Dari teknik berternak hingga penanganan penyakit, seharusnya berkonsultasi dengan ahlinya (dokter hewan) untuk mendapatan solusi terbaik dari permasalahan tersebut agar peternak mendapatkan hasil yang maksimal Dalam hal ini sistem pakar dijadikan sebagai alternatif kedua dalam membantu mengatasi pemecahan masalah. sistem pakar ini memberikan informasi tentang berbagai jenis penyakit yang menyerang hewan sapi, sistem pakar ini menggunakan metode Forward Chaining, dimana Sistem ini diharapkan dapat membantu dalam penanganan konsultasi peternak sapi dalam mendiagnosa penyakit. Sistem ini disediakan untuk memberi peluang peternak agar dapat membantu mengetahui gejala sebelum ditangani dokter hewan dan dapat mencegah gejala penyakit tersebut secara cepat.
DIAGNOSA AUTISME PADA ANAK DENGAN SISTEM PAKAR MENGGUNAKAN METODE FORWARD CHAINING Fadlan Isa Damanik; Said Iskandar Al-Idrus
Journal of Student Research Vol. 1 No. 2 (2023): Maret : Journal of Student Research
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jsr.v1i2.1063

Abstract

Gangguan mental pada anak merupakan gangguan kesehatan yang terdiri dari keterbelakangan mental autis dan anxiety disorder. Banyak orang awam yang tidak menyadari bahwa anaknya mengidap gangguan autisme. Ketidaktahuan ini disebabkan oleh kurangnya informasi tentang gangguan tumbuh kembang anak, gejalanya, dan kurangnya dokter spesialis tumbuh kembang anak dan psikolog. Penyebab autisme sendiri sudah ada sebelum bayi lahir, bahkan sebelum vaksinasi. Sistem pakar adalah cabang AI (Artificial Intelligence) yang menggunakan keahlian secara luas untuk memecahkan masalah. Pakar adalah seseorang yang memiliki keahlian dalam bidang tertentu dan memiliki pengetahuan atau keterampilan khusus yang orang lain di bidangnya tidak tahu atau tidak mampu. Berdasarkan kondisi di atas, maka dibangunlah sebuah sistem yang menggunakan teknologi komputerisasi yang dapat mengadopsi kemampuan seorang ahli atau pakar yaitu teknologi Artificial Intelligence atau Kecerdasan Buatan.
ENHANCING LQ45 STOCK PRICE FORECASTING USING LSTM MODEL Sinaga, Marlina Setia; Iskandar, Said; Manullang, Sudianto; Arnita, Arnita; Marpaung, Faridawaty; Buulolo, Fatizanolo
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.
Application of the K-Nearest Neighbor (K-NN) Algorithm for Detecting Banana Harvest Feasibility Citra Citra; Arnah Ritonga; Arnita Arnita; Said Iskandar Al Idrus; Debi Yandra Niska
J-INTECH ( Journal of Information and Technology) Vol 13 No 02 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i02.2064

Abstract

This study focuses on detecting banana harvest feasibility at the green-ripe stage, an area often overlooked in previous studies that focus only on general ripeness. The objective of this research was to develop a system based on the K-Nearest Neighbor (K-NN) algorithm to classify bananas as “Ready for Harvest” or “Not Ready for Harvest” using digital image processing. The system utilizes Hue Saturation Value (HSV) for color analysis and Gray Level Co-occurrence Matrix (GLCM) for texture identification. Unlike other methods, the combination of HSV and GLCM provides richer, complementary features, improving classification accuracy. The study was conducted at a banana plantation in Kwala Bekala Village, Medan Johor District, with 200 banana images taken from five different locations. The K-NN algorithm, with a value of K = 3, was chosen to avoid tie votes and ensure computational efficiency. The system achieved an accuracy of 94%, with precision of 93.5%, recall of 92.8%, and an F1-score of 93%. In beta testing with 33 respondents (18 farmers and 15 non-farmers), the system achieved a user satisfaction rate of 90%. Misclassifications occurred due to factors such as lighting variations and background noise. The study demonstrates the practical benefit of using the K-NN algorithm for determining the optimal harvest time, helping farmers make more accurate decisions, reducing waste, and increasing market competitiveness. This research fills the gap in detecting green-ripe bananas, providing an innovative solution to optimize harvest timing in the agricultural industry.
Identifikasi Tandan Buah Segar (TBS) Kelapa Sawit Layak Jual dengan Algoritma K-Nearest Neighbors Dechy Deswita Indriani.S; Kana Saputra S; Said Iskandar Al Idrus; Susiana Susiana; Adidtya Perdana
J-INTECH ( Journal of Information and Technology) Vol 13 No 02 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i02.2066

Abstract

Indonesia is the world's largest palm oil producer, with annual production reaching more than 45 million tons. The quality of oil palm fresh fruit bunches (FFB) determines the quality of the oil produced. The quality of FFBs can be seen through their maturity and health. Fruit that is not ripe, overripe, or contaminated with mold can reduce oil quality due to high levels of free fatty acids (FFA). This research aims to build a classification model of FFB marketability using the K-Nearest Neighbors (K-NN) algorithm with RGB and GLCM features. Image data was collected from the plantation, then processed through the stages of preprocessing, feature extraction, and normalization. The model was tested in three approaches, namely using RGB-GLCM combination features, RGB only, and GLCM only, with various data sharing scenarios, namely 70:30, 80:20, and 90:10, as well as varying k values, namely k = 3, 5, 7, 9. The evaluation results show that the RGB-GLCM feature combination model in the 80:20 data sharing scenario and k = 5 value is the most optimal model, with accuracy reaching 88%. In addition to providing high accuracy, this model also shows good stability compared to the RGB and GLCM models alone. This proves that the use of a combination of features is more effective and reliable in identifying the marketability of oil palm FFB compared to the use of a single feature.
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.
Penerapan Metode SMART Pada Sistem Pendukung Keputusan Penentuan Penerima Bantuan Sosial Bagi Keluarga Miskin Ada Novisari D. Simanungkalit; Nerli Khairani; Zulfahmi Indra; Said Iskandar Al Idus
bit-Tech Vol. 7 No. 2 (2024): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i2.1814

Abstract

Kemiskinan merupakan permasalahan utama yang dihadapi banyak negara di dunia, termasuk Indonesia, yang menghambat tercapainya kesejahteraan masyarakat karena berdampak pada rendahnya kualitas sumber daya manusia dan sulitnya memenuhi kebutuhan dasar. Salah satu upaya untuk mengatasi kemiskinan adalah melalui pemberian bantuan sosial kepada keluarga miskin. Namun, penyaluran bantuan ini sering kali tidak berjalan optimal akibat data penerima yang tidak akurat, sehingga memicu konflik dan protes. Penelitian ini bertujuan untuk menentukan kelayakan penerimaan bantuan sosial bagi keluarga miskin di Gereja Bethel Pembaruan Duri dengan menggunakan Metode SMART (Simple Multi Attribute Rating Technique). Penelitian ini dilakukan melalui beberapa tahapan, seperti identifikasi masalah, studi lapangan, kajian literatur, analisis, penerapan metode, serta pengujian dan validasi hasil. Data penelitian mencakup informasi dari 70 jemaat, meliputi nama, alamat, jumlah tanggungan, status pernikahan, tingkat pendidikan kepala keluarga, penghasilan, dan pengeluaran rumah tangga. Hasil penelitian menunjukkan bahwa jemaat dengan nilai akhir >=0,65 layak menerima bantuan, nilai antara >= 0,50 hingga  <=0,64 dipertimbangkan lebih lanjut berdasarkan jumlah tanggungan dan penghasilan, sementara nilai <= 0,49 dianggap tidak layak. Penelitian ini menyimpulkan bahwa Metode SMART efektif dalam menentukan kelayakan penerimaan bantuan sosial, karena mampu meminimalkan masalah ketidakakuratan data penerima, sehingga membantu meningkatkan efisiensi dan keadilan dalam proses distribusi bantuan sosial.
Flood Prediction for the Wampu River Basin Using the Simple Additive Weighting Method:A Case Study of the Wampu River in Bahorok Miftahul Janna; Said Iskandar; Arnita; Zulfahmi Indra; Susiana
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.v5i3.2268

Abstract

Flood is one of the natural disasters that frequently occurs in the Wampu Watershed (DAS Wampu), especially in Bahorok District. Flood risk is influenced by several factors such as rainfall, slope gradient, land use changes, and river depth. The problem in this study is the absence of a decision support system that can objectively determine flood risk levels. This study aims to determine the criteria and weights of flood risk, apply the Simple Additive Weighting (SAW) method, and analyze the accuracy level of the SAW method in determining flood risk. The method used in this research is the Simple Additive Weighting (SAW) method through several stages including criteria weighting, decision matrix construction, data normalization, preference value calculation, and alternative ranking. The research data consists of 18 villages with four criteria: rainfall, slope gradient, land use change, and river depth. The results show the classification of flood risk levels into high, medium, and low categories based on the obtained preference values. Villages with the highest preference values indicate a higher level of flood vulnerability compared to other villages. The model evaluation results indicate that the SAW method has an accuracy level of approximately 90% in determining flood risk classification. Based on these results, it can be concluded that the SAW method can be used as a decision support system to determine flood risk levels and provide recommendations for priority flood mitigation areas in Bahorok District.
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 Ada Novisari D. Simanungkalit Adidtya Perdana Ahmad Landong Alfattah Atalarais Ananda Hatmi, Reza Angga Warjaya Arifin, Khusnul Arnah Ritonga Arnita Arnita Arnita Arnita Arnita Arnita Arnita Arnita Ary Prandika Siregar Asiah Asiah Billroy A Ginting Buulolo, Fatizanolo Chairunisah Chairunisah, Chairunisah Citra Citra Debi Yandra Niska Dechy Deswita Indriani.S Devi Juliana Napitupulu Diah Retno Wahyuningrum Dian Septiana DIdi Febrian Eka Nainggolan, Rinay Eko Prasetya, Eko Elvis Napitupulu, Elvis Fadlan Isa Damanik Fadlan Isa Damanik Farhan Ramadhan, Haikal Fauziyah Harahap Fira Dilla Fitria, Amanda Hermawan Syahputra Ichwanul Muslim Karo Karo Ihsan Zulfahmi Inna Muthmainnah Insan Taufik Izwita Dewi Josafat Simanjutak, Todo Josua Christian Kana Saputra S Kana Saputra S Kuraini, Atifa Nuzulul Lazuardi Lazuardi Lubis, Afiq Alghazali Luge, Miclyael M. Revano Ananda Lubis Malik Fajri, Maulana MANSUR AS Manullang, Sudianto Manurung, Jeremia Marpaung, Faridawaty Miftahul Janna Mika . Layakana 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 Salsabila, Aqila Siburian, Rulli Prasetio Bane Sihombing, Jeremia Jordan Simamora, Elmanani Simanjorang, Rio Givent A Simbolon, Mula Tua Elia Sinaga, Marlina Setia Siregar, Mochammad Gani Alfa Alkhoiri Sri Mulyana Sri Mulyana Suryani, Nita Susiana Susiana Susiana Susiana Susiana Syarida Aini, Desti Tarigan, Dewan Dinata Tarigan, Yosua Yosephine 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