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All Journal Jurnal Sains dan Teknologi: Jurnal Keilmuan dan Aplikasi Teknologi Industri Jurnal Teknik dan Informatika Majalah Ilmiah UPI YPTK Journal of Computer Scine and Information Technology KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Abdimas ADPI Sains dan Teknologi Jurnal Ipteks Terapan : research of applied science and education Jurnal Sains Informatika Terapan (JSIT) Jurnal Komtekinfo Jurnal Teknik dan teknologi tepat guna
Dinul Akhiyar
Universitas Putra Indonesia “YPTK” Padang
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Journal : Jurnal Sains Informatika Terapan (JSIT)

 1 
Penerapan Aplikasi Supply Chain Management (SCM) Untuk Pendistribusian dan Stock Kerupuk Jangek “Nila” Berbasis Web Dinul Akhiyar; Radiyan Rahim
Jurnal Sains Informatika Terapan Vol. 1 No. 3 (2022): Jurnal Sains Informatika Terapan (Oktober, 2022)
Publisher : Riset Sinergi Indonesia (RISINDO)

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

Abstract

Supply Chain Management (SCM) is the activity stream of goods that includes planning, providing, production, storage, taransportasi, and distribution of, ranging from the initial point of raw materials (upstream) to the point of usage (downstream). The purpose of this paper is to provide knowledge on supply chain management as well as the components contained within the supply chain management, and implementation of the company is the effort karupuak jangek mintu, in helping the company's operations daily. Starting from the supply of raw materials to finished products (karupuak jangek), and activities relating to the distribution of products karupuak jangek up to the consumer. So that the necessary supply chain management to support and coordinate all of these requirements
Penerapan Aplikasi Supply Chain Management (SCM) Untuk Pendistribusian dan Stock Kerupuk Jangek “Nila” Berbasis Web Akhiyar, Dinul; Rahim, Radiyan
Jurnal Sains Informatika Terapan Vol. 1 No. 3 (2022): Jurnal Sains Informatika Terapan (Oktober, 2022)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v1i3.127

Abstract

Supply Chain Management (SCM) is the activity stream of goods that includes planning, providing, production, storage, taransportasi, and distribution of, ranging from the initial point of raw materials (upstream) to the point of usage (downstream). The purpose of this paper is to provide knowledge on supply chain management as well as the components contained within the supply chain management, and implementation of the company is the effort karupuak jangek mintu, in helping the company's operations daily. Starting from the supply of raw materials to finished products (karupuak jangek), and activities relating to the distribution of products karupuak jangek up to the consumer. So that the necessary supply chain management to support and coordinate all of these requirements
Penerapan Sistem Pakar Dalam Diagnosa Pengguna Narkoba Menggunakan Metode Naïve Bayes Marfalino, Hari; Pratiwi, Mutiana; Arief Wisky, Irzal; Akhiyar, Dinul
Jurnal Sains Informatika Terapan Vol. 2 No. 2 (2023): Jurnal Sains Informatika Terapan (Juni, 2023)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v2i2.159

Abstract

Perkembangan teknologi saat ini terasa sangat membantu pengguna dalam hal apapun. Tidak terkecuali membantu pihak berwajib dalam menentukan seseorang melakukan penyalahangunaan narkoba. Penyalahgunaan narkoba merupakan masalah serius yang mempengaruhi Kesehatan dan kualitas hiduo individu. Penelitian ini bertujuan untuk mengembangkan sebuah sistem pakar yang dapat membantu dalam proses diagnosa pengguna narkoba menggunakan metode Naïve Bayes. Metode Naïve Bayes adalah salah satu metode klasifikasi yang berdasarkan teorema bayes dengan asumsi bahwa semua atribut yang digunakan diklasifikasi adalah independent. Atribut yang digunakan dalam penelitian ini adalah usia, jenis kelamin, Riwayat pengguna narkoba, dan gejala. Penelitian ini menggunakan data dari individu yang telah terdiagnosis sebagai pengguna narkoba. Hasil penelitian ini adalah menghasilkan diagnosa jenis narkoba yang dikonsumsi dengan nilai akurasi. Terdapat salah satu pengguna narkoba yang terdiagnosa penyalahgunaan narkoba jenis Sabu dengan nilai akurasi 0.4468.
DECISION SUPPORT SYSTEM FOR SCHOLARSHIP RECIPIENT SELECTION USING THE SIMPLE ADDITIVE WEIGHTING (SAW) METHOD Mardhiah, Putri; Pratiwi, Mutiana; Akhiyar, Dinul; Arsyah, Ulya Ilhami
Jurnal Sains Informatika Terapan Vol. 3 No. 2 (2024): Jurnal Sains Informatika Terapan (Juni, 2024)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v3i2.380

Abstract

The advancement of science facilitates the development of new technologies that signify the progress of society. MTsS Muhammadiyah Kurai Taji aims to incorporate information technology into its data processing activities. Currently, the school relies on manual methods for processing student data, which often results in inaccuracies, particularly in classifying underprivileged students and other categories. This manual approach has led to challenges in maintaining valid data, which in turn complicates the decision-making process for scholarship allocations. To address these issues, the author proposes the development of a web-based A decision Support System (DSS). A DSS is a computer-based information system designed to support organizational decision-making. The proposed system will utilize MySQL database management to ensure the accuracy and validity of the data. By implementing this web-based information system, the school will benefit from increased time efficiency in data retrieval and scholarship processing. Additionally, this system will streamline reporting processes and improve the identification of students eligible for scholarship assistance, thereby addressing the current challenges faced by the school.
The Application Of Big Data And Artificial Intelligence In Business Decision-Making Based On Infocom Technology Akhiyar, Dinul; Nofriadiman
Jurnal Sains Informatika Terapan Vol. 3 No. 3 (2024): Jurnal Sains Informatika Terapan (Oktober, 2024)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v3i3.427

Abstract

The rapid development of Information and Communication Technology (Infocom) has led to significant advancements in business decision-making, particularly through the application of Big Data and Artificial Intelligence (AI). This paper explores how these technologies are utilized to enhance business decision-making processes, providing organizations with valuable insights and predictive capabilities. Big Data refers to the vast amount of structured and unstructured data generated from various business activities, while AI leverages algorithms and machine learning techniques to analyze this data, uncover patterns, and make informed predictions. The integration of Big Data and AI enables businesses to improve efficiency, optimize resource allocation, and gain a competitive edge in a dynamic market environment. The paper discusses various applications, including predictive analytics, customer segmentation, and supply chain optimization, highlighting their impact on strategic and operational decision-making. Moreover, it addresses the challenges associated with the implementation of these technologies, such as data quality, privacy concerns, and the need for skilled professionals. Ultimately, the study emphasizes that the synergy between Big Data and AI, when effectively implemented, can drive data-driven business strategies and foster innovation.
Risk Prediction Of Coronary Heart Disease Using A Decision Tree Algorithm Based On Patient Medical Records Akhiyar, Dinul; Nofriadiman; Rahim, Radiyan; Firdaus
Jurnal Sains Informatika Terapan Vol. 4 No. 2 (2025): Jurnal Sains Informatika Terapan (Juni, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i2.568

Abstract

Coronary heart disease (CHD) remains one of the leading causes of death worldwide, often due to late diagnosis and inadequate early detection. Early risk prediction of CHD is crucial to improve patient outcomes and reduce mortality. This study aims to develop a predictive model for assessing the risk of coronary heart disease using a decision tree algorithm, based on structured patient medical records. The dataset used contains various clinical features, including age, gender, cholesterol level, blood pressure, blood sugar, ECG results, and exercise-induced angina. A decision tree classifier was selected for its interpretability, ease of implementation, and effectiveness in handling categorical and numerical data. Data preprocessing steps such as missing value handling, normalization, and feature selection were applied to improve model performance. The model was trained and validated using k-fold cross-validation to ensure reliability. Performance was evaluated based on accuracy, precision, recall, and F1-score. The results demonstrate that the decision tree algorithm achieved satisfactory performance in predicting CHD risk, making it a potentially valuable tool for supporting clinical decision-making. This study highlights the importance of integrating data mining techniques into healthcare to enable timely and accurate risk assessment of life-threatening diseases such as coronary heart disease.
Adaptasi spesifik efficientnetb0 dengan lapisan kustom untuk identifikasi buah tropis Akhiyar, Dinul; Herasmus, Hilda; Nofriadiman
Jurnal Sains Informatika Terapan Vol. 4 No. 2 (2025): Jurnal Sains Informatika Terapan (Juni, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i2.727

Abstract

Identifikasi buah tropis berbasis citra digital menghadapi tantangan multidimensi akibat keragaman morfologi intra-kelas (seperti variasi tingkat kematangan pada pisang) dan kesamaan visual antar-kelas (misalnya kemiripan geometris antara mangga dan nanas), diperparah oleh kondisi lingkungan lapangan yang tidak terkontrol seperti pencahayaan dinamis dan latar belakang kompleks. Untuk mengatasi masalah ini, penelitian ini mengusulkan strategi adaptasi spesifik domain pada arsitektur EfficientNetB0 melalui integrasi blok lapisan kustom yang terdiri dari Dense Layer 256-neuron dengan aktivasi swish, normalisasi lapisan (Layer Normalization), dan Spatial Dropout 0.3, serta mekanisme kalibrasi bertahap (gradual unfreezing) yang membuka lapisan konvolusional secara progresif. Dataset sebanyak 5.200 citra buah tropis Indonesia (pisang, mangga, nanas, durian, rambutan) diperkuat dengan teknik augmentasi dinamis berbasis domain knowledge, termasuk color jitter terarah dan random erasing untuk meniru variasi kondisi riil. Hasil eksperimen menunjukkan pencapaian akurasi validasi 88.7% dan F1-score rata-rata 0.87, yang mengungguli kinerja MobileNetV2 sebesar 6.4% dalam uji komparatif. Implementasi operasional dalam sistem FruitScan-ID membuktikan efektivitas metode ini dengan mengurangi kesalahan identifikasi manual hingga 40%, menawarkan solusi komputasi tepi (edge-computing) yang hemat sumber daya untuk otomasi industri pertanian tropis.
Co-Authors Abulwafa Muhammad Aqil Ramadhan, Muhammad Arsyah, Ulya Ilhami Buyung Septyanta, Andriyan Firdaus Hakim, Wijaya Herasmus, Hilda Hidayatullah, Genta Magribi Irzal Arief Wisky Irzal Arif Wisky Iskandar, Riki Mardhiah, Putri Marfalino, Hari Muhammad Akhif Muhammad, Abulwafa Nofriadiman Nori Sahrun Pratiwi, Mutiana Putra, Ondra Eka Radiyan Rahim Radiyan Rahim Rahim, Radiyan Rahmi, Nadya Alinda Rahmi Riki Iskandar Riki Iskandar Riki Iskandar Silfia Andini Sumijan Teri Ade Putra Vivi Nila Sari