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Application of the analytic network process method in the selection of raw material suppliers for yarn Gunawan, Gunawan; Hafid Subechi, Fadlan; Arif, Zaenul
Jurnal Mantik Vol. 8 No. 1 (2024): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v8i1.5227

Abstract

This study applies the Analytic Network Process (ANP) Method for selecting raw material suppliers for yarn, a crucial factor in boosting production efficiency and quality within the textile industry. The research aims to develop and validate a decision-making model that enhances supplier selection by integrating ANP with rigorous quantitative analyses. The methodology incorporates a series of experiments, thorough examination of historical data, and robust model validation processes to confirm the accuracy and dependability of the findings. The results demonstrate significant improvements in the precision of supplier selection, underscored by a high Pearson correlation coefficient of 0.89. This validates the model's effectiveness and reliability, suggesting that the developed framework not only supports data-driven and objective decision-making in the textile industry but also has potential applications in other sectors to enhance operational efficiency and sustainability
Application of the rule-based system method to determine the type of crops based on altitude and rainfall Gunawan, Gunawan; Firmansyah, Akhmad Lutfi; Santoso, Bayu Aji
Jurnal Mantik Vol. 8 No. 1 (2024): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v8i1.5234

Abstract

Applying the rule-based system method to determine the type of agricultural crop based on altitude and rainfall is essential in increasing productivity and efficiency in modern agriculture. This study aims to develop and implement a rules-based system to recommend suitable plant types by analyzing altitude and rainfall data in the Tegal District. The research method includes experimental design, quantitative analysis, and model validation using data from the Central Bureau of Statistics and various other internet sources, covering January 1 to December 31, 2023. The results showed that this rule-based system effectively provides accurate recommendations with an average accuracy rate of 85% and an error rate of 15%. This system helps farmers make informed decisions about crop selection, reducing crop failure risk and contributing to sustainable agricultural practices. Future research suggests integrating real-time weather prediction technology and additional environmental variables to improve the precision of recommendations and expand the applicability of these systems to other areas with similar characteristics
Expert system for diagnosing diseases in corn plants using the navies bayes method Gunawan, Gunawan; Sya’bani, Adhita Zulfa; Anandianshka, Sawaviyya
Jurnal Mantik Vol. 8 No. 1 (2024): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v8i1.5265

Abstract

This research introduces an expert system using the Naive Bayes method to diagnose corn plant diseases, aiming to provide an automated, accurate, and scalable diagnostic tool. Traditional methods are often inefficient and error-prone, relying on expert knowledge and manual inspection. This study employs a quantitative approach, incorporating experimental design, data analysis, and model validation. Data on humidity, temperature, and soil conditions were collected from agricultural research centers and online databases. After preprocessing, key variables influencing disease occurrence were selected. The Naive Bayes model was optimized using cross-validation and implemented in Python, achieving an average accuracy of 92%. The model's performance, evaluated through accuracy, precision, recall, and F1-score, demonstrated the effective distinction between similar symptoms—the system's simplicity and computational efficiency suit resource-constrained environments like rural farms. By combining visual symptoms and environmental factors, the system minimizes dependency on expert knowledge, offering a comprehensive and scalable solution for disease management in agriculture
Application of k-nearest neighbors method for detection of beef authenticity based on beef image gunawan, Gunawan; Moonap, Dinar Auranisa; Fadhilah, Nurul
Jurnal Mantik Vol. 8 No. 1 (2024): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v8i1.5281

Abstract

Beef authenticity detection is a significant concern in today's food industry. This study proposes the K-Nearest Neighbors (K-NN) method based on the extraction of the Histogram of Oriented Gradients (HOG) feature to detect the authenticity of beef based on images. A dataset of 40 images of real and fake beef was collected and aggregated into 240 images to increase the variety of data. The imagery is changed to grayscale, and the HOG feature is extracted to capture texture and shape information. The K-NN model is built with optimized parameters using Grid Search and cross-validation techniques. The model was evaluated by measuring accuracy, precision, recall, and F1-score on the test data. The results show that the K-NN model with HOG feature extraction can achieve an accuracy of 80.56%,  precision of 87.10%, recall of 72.97%, and F1-score of 72.97% in classifying real and fake beef. These findings confirm the effectiveness of the proposed method for the rapid and accurate detection of beef authenticity. This research contributes to developing image-based food authenticity detection methods that can be applied to increase consumer confidence in the food industry
KLASIFIKASI EFEK KERUSAKAN GEMPA BUMI BERDASARKAN SKALA MODIFIED MERCALLI INTENSITY MENGGUNAKAN ALGORITMA MULTICLASS SUPPORT VECTOR MACHINE Wahyu Pratama, Raka; Herry Chrisnanto, Yulison; Gunawan, Gunawan
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 2 (2024): JATI Vol. 8 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i2.9211

Abstract

Gempa bumi merupakan bencana alam yang sering terjadi di Indonesia karena letak geografisnya yang berada di pertemuan tiga lempeng tektonik besar. Data gempa bumi diperoleh dari Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) dalam 2 tahun terakhir pada periode tahun 2022 hingga 2023, dan terdapat 1.689 record data yang tersedia, dengan atribut seperti lokasi, magnitudo, kedalaman, dan efek gempa. Pada data yang diperoleh menunjukkan ketidakseimbangan yang signifikan pada antar kelas dengan itu dilakukan teknik oversampling menggunakan SMOTE untuk menyeimbangkan jumlah sampel pada setiap kelas. Konsep Support Vector Machine (SVM) yang paling dasar, metode ini dapat melakukan klasifikasi biner dengan memisahkan titik data menjadi dua kelas, namun tidak mendukung klasifikasi multikelas secara bawaan. Penelitian ini bertujuan untuk mengimplementasikan pendekatan algoritma Multiclass Support Vector Machine (SVM) dengan membandingkan kernel non-linear dalam mengklasifikasikan dampak gempa bumi berdasarkan skala Modified Mercalli Intensity (MMI). Eksperimen pengujian pada penelitian ini membandingkan Multiclass Support Vector Machine (SVM) One vs Rest dan One vs One dengan kernel non-linear seperti polynomial, rbf, dan sigmoid. Hasil penelitian ini menunjukkan bahwa pendekatan algoritma Multiclass Support Vector Machine (SVM) One vs Rest dengan kernel rbf lebih baik dari pendekatan dan kernel lainnya dengan hasil akurasi 51.13%, presisi 42.60%, recall 52.46%, dan f1-score 42.51%.
PENERAPAN METODE WAVELET DAUBECHIES DAN DIAGRAM NOHIS-TREE UNTUK KLASIFIKASI CONTENT BASED IMAGE RETRIEVAL BATIK Qurrotu Aini, Atikah; Alim Murtopo, Aang; Fadilah, Nurul; Gunawan, Gunawan
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 2 (2024): JATI Vol. 8 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i2.9441

Abstract

Penelitian ini mengevaluasi metode Daubechies Wavelet dan NOHIS-Tree Diagram untuk Pengambilan Gambar Berbasis Konten Batik, dengan fokus pada efektivitasnya dalam mengidentifikasi motif Batik. Bab ini membandingkan metode-metode ini dengan metode lain, mengeksplorasi validitas, potensi bias, dan keandalan data, serta membahas implikasi praktis untuk aplikasi dunia nyata dan manfaat industri. Kain batik, yang merupakan warisan budaya Indonesia, sering dikaitkan dengan kurangnya kesadaran masyarakat sehingga menyebabkan klaimnya sebagai budaya bangsa lain dalam beberapa tahun terakhir. Hal ini menunjukkan perlunya perhatian segera untuk mencegah kesalahpahaman tersebut. Metode pengenalan pola khususnya metode wavelet dengan jenis wavelet Daubechies digunakan untuk mengenali motif batik. Prosesnya diawali dengan input citra batik dalam skala abu-abu, dilanjutkan dengan dekomposisi hingga diperoleh koefisien wavelet. Nilai energi dan entropi wavelet dihitung, dan nilai masukan dibandingkan dengan nilai database. Semakin kecil nilai errornya maka semakin mirip gambar tersebut. Penelitian ini menyelidiki penggunaan metode Daubechies Wavelet dan NOHIS-Tree Diagram pada aplikasi Image Retrieval Berbasis Konten Batik. menggunakan Batik dalam berbagai format dan menggunakan variasi wavelet tatanan Daubechies sebagai ekstraktor fitur. Diagram Pohon NOHIS digunakan sebagai sistem identifikasi Batik. Penelitian ini menggunakan software MATLAB R2011b untuk mengukur kinerja metode. Tujuannya adalah untuk berkontribusi terhadap pengembangan pemrosesan digital, khususnya dalam identifikasi Batik, dan membantu pembuatan perangkat lunak yang dapat mengenali pola Batik dengan peningkatan akurasi dan efektivitas. Hasil dari penelitian menunjukkan bahwa aplikasi CBIR Batik dengan menggunakan Wavelet Daubechies dan Diagram NOHIS-Tree memiliki tingkat akurasi tertinggi bahwa aplikasi Content Based Image Retrieval motif batik menggunakan WaveletDaubechies memliki tingkat akurasi 100% pada hasil uji 9 teratas, 100% pada hasil uji 6 teratas, 80% pada hasil uji 3 teratas, dan40% pada hasil uji 1 teratas.
Analisis Perbandingan Model Jaringan Saraf Tiruan dan Support Vector Machine dalam Memprediksi Indeks Harga Saham Gabungan Gunawan, Gunawan; Andriani, Wresti; Wibowo, Septian Ari
Pena: Jurnal Ilmu Pengetahuan dan Teknologi Vol. 38 No. 2 (2024): PENA SEPTEMBER 2024
Publisher : LPPM Universitas Pekalongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31941/jurnalpena.v38i2.4942

Abstract

The Jakarta Composite Index (IHSG) is a key indicator that reflects the performance of the stock market in Indonesia. It is often used by investors, analysts, and decision-makers to assess economic conditions and make investment decisions. However, the fluctuating and dynamic nature of the stock market makes predicting the IHSG a significant challenge. This study compares the effectiveness of Neural Network (NN) and Support Vector Machine (SVM) with optimization methods such as Particle Swarm Optimization (PSO) and Evolutionary Algorithm (EVO) in predicting stock prices. The results show that the combination of SVM with EVO provides the best prediction accuracy with the lowest error values (RMSE: 0.07, MAE: 0.09, MSE: 0.004). In contrast, NN with PSO and EVO showed higher prediction errors, indicating lower accuracy levels. These findings highlight the potential of optimization methods in enhancing the performance of stock prediction models, with SVM+EVO being the most effective combination.
Application of machine learning for election data classification in Tegal city based on political party support Andriani, Wresti; Gunawan, Gunawan; Naja, Naella Nabila Putri Wahyuning; Anandianskha, Sawaviyya
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 4 (2024): December: Intelligent Decision Support System
Publisher : Institute of Computer Science (IOCS)

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

Abstract

Elections are a critical aspect of democracy, where voter sentiment and political party support significantly influence outcomes. This study aims to predict election results in Tegal City using machine learning models, specifically Neural Networks, Random Forest, and Naive Bayes. Each algorithm was applied to a dataset containing demographic, polling, and Sentiment data to analyze political party support. The research revealed that Neural Networks outperformed other models in terms of accuracy (92%) and F1 scores for both positive (91%) and negative sentiments (92%). Random Forest and Naive Bayes, while effective, displayed lower overall performance. The findings highlight the value of utilizing advanced algorithms for local election sentiment analysis to help candidates adjust campaign strategies. This approach enhances understanding of voter behavior and supports more informed decision-making processes for the public and policymakers
Penerapan algoritma K-Nearest Neighbors (KNN) untuk klasifikasi citra medis Ujianto, Nur Tulus; Gunawan; Fadillah, Haris; Fanti, Azizah Permata; Saputra, Aryan Dandi; Ramadhan, Ilham Gema
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 4 No 1 (2025): IT-Explore Februari 2025
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/itexplore.v4i1.2025.pp33-43

Abstract

This study aims to optimize the implementation of the K-Nearest Neighbors (K-NN) algorithm for medical image classification by focusing on selecting the optimal KKK parameter and applying dimensionality reduction techniques to improve accuracy and efficiency. The data used was sourced from public medical image repositories such as The Cancer Imaging Archive (TCIA) and Medical Image Analysis datasets, covering various diseases, including brain tumors, lung cancer, and kidney lesions. The research process involves data collection, data preprocessing, dimensionality reduction using Principal Component Analysis (PCA), applying the K-NN algorithm with Euclidean, Minkowski, and Cosine distance metrics, and performance evaluation using accuracy, precision, recall, and F1-score. Experimental results demonstrate that K=5with the Euclidean distance metric provides the best performance, achieving an accuracy of 90%. Additionally, PCA effectively reduces computational time by 30% without significantly compromising accuracy. This study proves that K-NN is an effective method for medical image classification. However, further research is needed to integrate K-NN with deep learning models to enhance performance and feature extraction capabilities.
Review Penerapan Smart City dalam Sistem Informasi Desa Gunawan, Gunawan; Kurniawan, Yan; Andriani, Wresti
Jurnal Teknik Indonesia Vol. 1 No. 1 (2022): Jurnal Teknik Indonesia
Publisher : Publica Scientific Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (514.283 KB) | DOI: 10.58860/jti.v1i1.5

Abstract

Introduction: The industrial era 4.0 and civil society 5.0 have changed the paradigm of culture, especially in rural communities thatnot only want innovation in sustainable rural development but in the form of administrative and non-administrative services carried outby the Village Government wanting excellent service. The community paradigm that wants everything to be on time and to fulfill the need for good service is the desire and hope of the community. So that a solution is needed from the Village Government, oneof which is the application of the intelligent village concept,which is based on Total Quality Service (TQS) and remains focused on service satisfaction for customers (village communities). Purpose: to analyze the review of the implementation of smart cities in village information systems. Methods: The method used is a qualitative research method with the research locus in several villages in the Pemalang Regency area. Results: The intelligent village concept has been appliedin several towns in the Pemalang Regency area,which is oriented to deliveringvillage information and assisting in providing services to the community. The results achieved are prettygood but require improvements in the applied management information system. Conclusion: The PemalangRegency Government utilizes rural sites/websites facilitated by PUSPINDES to develop the Pemalang area. Conduct village website management training. It usesICT for public information disclosure. Utilize ICT as a forum for village information and village promotion.
Co-Authors Aang Alim Murtopo Aditdya, Maulana Ahmad Zulfikri Aimar Akbar, Aminnur Aisyach Aminarti Santoso Al Fattah, Muhammad Raikhan Alan Eka Prayoga Albana, Muhammad Syifa Ali Murtopo, Aang Amalani, Mukhamad Zulfa Bakhtiar Ananda, Pingky Septiana Anandaianskha, Sawaviyya Anandianshka, Sawaviyya Anandianska, Sawaviyya Anandianskha, Sawaviyya Andriani, Wresti Andriani, Wresty Anshori, Abu Hasan Al Arianti, Tezya Sekar Arif, Zaenul Arifiyah, Nur Latifatul Arrohman, Zidni Dlia Aslam, Muhammad Nur Aziz, Taufiq Azmi, Isni Azmi, Muchamad Nauval Bangkit Indarmawan Nugroho Budiono, Wahyu Cahyo, Septian Dwi Catur Supriyanto Dari, Mayang Melan Dewi, Errika Mutiara Didiek Trisatya Dodi Setiawan Dodi Setiawan Dwi Fina Fahirah Dwi Kurniawan, Rifki Fadila, Nurul Fahirah, Dwi Fina Fanti, Azizah Permata Farkhan, Muhammad Fatkhurrohman Fatkhurrohman, Fatkhurrohman Firmansyah, Akhmad Lutfi Firmansyah, Hasbi Firmansyah, Muchamad Aries Gunawan Gunawan Hafid Subechi, Fadlan Handayani, Sri Harefa, Reyvan Sinatria Haris Fadillah Hassan, Muhamad Nur Hidayatullah, Bryan Adam Intan Mayla Faiza Intan Mayla Faiza Januarto, Sigit Khadziqul Humam Munfi Khasanah, Apriliani Maulidya Khusni, Muhammad Wazid Kurniawan, Rifki Dwi Lestari, Nindy Putri Limaknun, Lulu Lutfayza, Rezi Marzuqi, Maezun Nafis Maulana, M Taufik Fajar Miftakhuddin, Ahmad Miftakhudin, Muhammad Milkhatunisya, Milkhatunisya Moonap, Dinar Auranisa Muchamad Nauval Azmi Muh Ridwan Muhammad Sulthon Mutaqin, Ahadan Fauzan Muttaqin, Anik Naja, Naella Nabila Putri Wahyuning Ningrum, Isna Lidia Nughroho, Bangkit Indarmawan Nugroho Adhi Santoso Nugroho, Bangkit Indramawan Nur Aisyah Nur Tulus Ujianto Nurokhman, Akhmad Nursahid, Wahyu Nursidik, Maulia Nurul Fadhilah Nurul Fadilah, Nurul Prayoga, Alan Eka Priyo Haryoko Purwanto Purwanto Putra, Alif Sya’Bani Qurrotu Aini, Atikah Rafhina, Ana Ramadhan, Ilham Gema Rifki Dwi Kurniawan Rivaldiansyah, Rafik Riyadi, Fajar Sugeng Santoso, Aisyach Aminarti Santoso, Bayu Aji Santoso, Nughroho Adhi Santoso, Nugroho Adh Santoso, Nugroho Adhi Santoso, Nugroho Adi Saputra, Aryan Dandi Sarif Surorejo Sawaviyya Anandianskha Sawaviyya Anandianskha Sawaviyya Anandianskha Sawavyya Anandianskha Septian Ari Wibowo Septiana Ananda, Pingky Septiana, Pingky Setiawati, Windi Surur, Misbahu Sya’bani, Adhita Zulfa Syefudin, Syefudin Triwinanto, Mohammad Amin Triwinanto Ubaidillah, Muhamad Rizal Ujianto, Nur Tulus W.N, Naella Nabila Putri Wahyu Pratama, Raka Wahyuning Naja, Naella Nabila Putri Wilda Shabrina Wresti Andriani Wresti Andriani Wresti Andriani Yan Kurniawan Yan Kurniawan, Yan Yulison Herry Chrisnanto Zaenul Arif Zain Hidayatullah, Fikri Zain, Ahmad Muzakky