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IMPLEMENTASI ALGORITMA DIJKSTRA DALAM PENENTUAN JALUR TERPENDEK MENUJU OBJEK WISATA DI KABUPATEN TEGAL syefudin, Syefudin; Zain, Ahmad Muzakky; Gunawan, Gunawan
Jurnal Technopreneur (JTech) Vol 11 No 2 (2023): JURNAL TECHNOPRENEUR (November)
Publisher : UPPM Politeknik Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30869/jtech.v11i2.1233

Abstract

Pencarian jalur terpendek pada sebuah graf berbobot mengacu pada pencarian jalur dengan jumlah bobot simpul terkecil, yang membentuk jalur tersebut. Oleh karena itu, jalur yang ditemukan merupakan jalur yang memiliki jumlah bobot simpul atau jarak yang paling minim. Pencarian jalur terpendek dilakukan pada pencarian objek wisata di Kabupaten Tegal yang akan membentuk suatu graf berarah dan berbobot. Sebelum menjalankan algoritma Dijkstra, dibutuhkan data jarak dari setiap simpul terlebih dahulu untuk menentukan jalur terpendek. Objek wisata yang dituju merupakan objek wisata yang diminati warga atau wisatawan, terdapat satu objek wisata yang paling diminati di Kabupaten Tegal sesuai hasil survey yaitu, Objek Wisata Guci. Bobot tiap simpul diambil dari geolocation objek wisata Guci. Dari hasil proses algoritma Dijkstra dapat diketahui jalur yang dihasilkan oleh algoritma Dijkstra sudah cukup akurat atau tepat. Kemudian dari data jarak yang didapatkan, kemudian dihitung waktu tempuh menuju objek wisata Guci dengan memasukkan variable bobot kemacetan dan rata-rata kecepatan yang diasumsikan dari kendaraan roda 2 dan roda 4. Pada penelitian ini, perhitungan jalur terpendek rute terpendek yang didapatkan dari titik awal di Taman Rakyat Slawi menuju Objek Wisata Guci sejauh 28,7 km. Sedangkan data waktu tempuh didapat dari kondisi jalan yang diasumsikan normal tanpa hambatan kecuali bobot kemacetan.
ANALISIS KEAMANAN DAN PRIVASI DATA PADA LAYANAN CLOUD COMPUTING DENGAN MENGGUNAKAN TEKNIK KRIPTOGRAFI Riyadi, Fajar Sugeng; syefudin, Syefudin; gunawan, Gunawan; Murtopo, Aang Alim
Jurnal Technopreneur (JTech) Vol 11 No 2 (2023): JURNAL TECHNOPRENEUR (November)
Publisher : UPPM Politeknik Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30869/jtech.v11i2.1241

Abstract

Dunia teknologi tidak akan menjadi bahan perdebatan publik di negara Indonesia, dan pemerintah bekerja keras untuk meningkatkan kualitas dan nilai warisan yang menyandang nama tersebut. Artikel ini mengeksplorasi bagaimana layanan cloud, bahkan proyek sektor publik, dapat diamankan sehingga dapat diakses dari mana saja. Metode penelitian yang digunakan dalam penulisan ini meliputi pengumpulan data, studi kasus, tinjauan literatur, dan penelitian teoritis untuk mengumpulkan data guna mendukung penelitian dan menemukan bahan penelitian yang serupa untuk mendukung penelitian dan tinjauan. Keamanan data atau keamanan data adalah yang paling penting dalam layanan cloud, keamanan data pengguna mudah rusak di cloud dan tidak ada undang-undang untuk mengontrol pemadaman listrik. Kemungkinan pelanggaran data dan privasi dapat dihilangkan jika semua tindakan untuk meningkatkan keamanan data digunakan. Langkah-langkah keamanan yang diberikan adalah menyelesaikan proses sertifikasi, meningkatkan privasi dan kerahasiaan, serta membuat cloud computing dapat diandalkan dalam hal privasi. Kata kunci - komputasi awan, komputasi awan, pembelajaran, keamanan, perlindungan, keamanan data, otentikasi pengguna, enkripsi data, dan lapisan soket aman.
PENERAPAN COMPUTER VISION UNTUK MENDETEKSI KELENGKAPAN ATRIBUT SISWA MENGGUNAKAN METODE CNN Alim Murtopo, Aang; Aditdya, Maulana; Septiana Ananda, Pingky; Gunawan, Gunawan
PROSISKO: Jurnal Pengembangan Riset dan Observasi Sistem Komputer Vol. 11 No. 2 (2024): Prosisko Vol. 11 No. 2 September 2024
Publisher : Pogram Studi Sistem Komputer Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/prosisko.v11i2.8752

Abstract

Kepatuhan dalam mengenakan atribut sekolah secara lengkap merupakan salah satu aspek penting yang mencerminkan disiplin dan karakter siswa di lingkungan pendidikan. Namun, seringkali ditemukan pelanggaran terkait kelengkapan atribut siswa yang dapat berdampak negatif pada pembentukan budaya disiplin. Tujuan dari penelitian ini yaitu menerapkan metode Convolutional Neural Network (CNN) dalam mendeteksi kelengkapan atribut siswa seperti logo sekolah, logo osis, nametag, logo bendera, sabuk, dan sepatu dari gambar menggunakan computer vision. Penerapan teknologi computer vision dengan Convolutional Neural Network (CNN) dapat membantu pihak sekolah dalam menegakkan disiplin penggunaan atribut siswa secara lebih efektif, efisien, dan objektif. Pada penelitian ini, dataset gambar siswa yang mengenakan atribut lengkap dan tidak lengkap dikumpulkan untuk proses pelatihan model Convolutional Neural Network (CNN). Teknik augmentasi data dan pelabelan gambar dilakukan untuk mempersiapkan dataset yang digunakan. Model Convolutional Neural Network (CNN) dibangun dengan beberapa lapisan konvolusi, pooling, dense, dan fungsi aktivasi seperti ReLU dan Sigmoid. Selanjutnya, empat kali pengujian dilakukan dengan menggunakan data uji yang berbeda untuk mengevaluasi performa model. Hasil pengujian menunjukkan bahwa model Convolutional Neural Network (CNN) yang dibangun dapat mencapai rata-rata akurasi 93,34%, presisi 95%, recall 94,12%, dan F1-score 94,35% dalam mendeteksi kelengkapan atribut siswa. Temuan ini mengindikasikan bahwa metode Convolutional Neural Network (CNN) berpotensi besar untuk dimanfaatkan dalam mendeteksi kelengkapan atribut siswa secara otomatis, objektif, dan efisien, serta dapat berkontribusi pada upaya peningkatan disiplin dan pembentukan karakter di lingkungan sekolah.
Impact of Palestine-Israel conflict on multinational stock prices use neural network and support vector machine comparison Andriani, Wresti; Gunawan, Gunawan; Wahyuning Naja, Naella Nabila Putri
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.v8i1.5196

Abstract

One form of prolonged geopolitical event is the conflict between Palestine and Israel, which has complex historical, political, and religious roots in the Middle East. This research aims to determine whether this conflict influences the share prices of the companies Unilever, McDonald's, and KFC. These three large companies are known as allies of one of the disputing countries. The method used by the Neural Network is compared with Support Vector Machine to find the best accuracy using RMSE and MAE. The greater the error value, the more affected the company is by this geopolitical factor. As a result, the accuracy of the SVM method is better than NN; the company most affected is KFC, with the RMSE value of 0.111, MAE of 0.020, followed by Unilever with RMSE 0.034, MAE 0.025 then McDonald's with RMSE 0.026 and MAE 0.116, is expected to help investors choose to invest in the company McDonald’s then Unilever.
Performance evaluation of single moving average and exponential smoothing in shallot production prediction Santoso, Aisyach Aminarti; Surorejo, Sarif; Kurniawan, Rifki Dwi; Gunawan, Gunawan
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.v8i1.5205

Abstract

Shallots are a strategic commodity that has significant health benefits, including its ability to prevent cancer. The commodity also plays an important role in the agricultural economy, especially in Indonesia, where high demand in domestic and international markets contributes greatly to farmer’s income. However, fluctuations in shallot production often lead to price instability, which has a negative impact not only on consumers but also on the sustainability of farmers' income. This research aims to develop a forecasting model that can assist in more effective planning of shallot production. To achieve this goal, the study tested and compared two forecasting methods: Single Moving Average (SMA) and Single Exponential Smoothing (SES), which are known for their ease of implementation and accuracy in predicting time series data. Using a dataset of shallot production from Brebes Regency over the period 2020-2023, the study found that Single Exponential Smoothing consistently provided more accurate results than Single Moving Average. SES performance is more responsive to recent changes in production data, which is particularly important given the rapid fluctuations that often occur in the agricultural sector. The findings suggest that the application of the SES method in shallot production forecasting can facilitate more informed decision-making in production management and distribution planning, potentially stabilizing market prices and improving farmers' economic conditions
Application of machine learning for short-term climate prediction in Indonesia Gunawan, Gunawan; Andriani, Wresti; Aimar Akbar, Aminnur
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.v8i1.5215

Abstract

This study explores the Application of Machine Learning for Short-Term Climate Prediction in Indonesia, focusing on enhancing forecast accuracy through advanced computational models. The primary objective was to develop and validate Random Forest and Support Vector Machine (SVM) models to predict short-term climate conditions accurately across ten major Indonesian cities. Employing a quantitative approach, the study utilized experimental design, rigorous data analysis, and model validation using historical weather data from April 2024 provided by the Indonesian Meteorological, Climatological, and Geophysical Agency (BMKG). The results indicate that both Random Forest and SVM significantly outperform traditional climate prediction models, with Random Forest achieving an average accuracy of 87.5% and SVM 85.2%. These findings underscore the potential of machine learning to revolutionize short-term climate predictions in regions with complex meteorological dynamics like Indonesia, offering substantial implications for disaster preparedness, agricultural planning, and urban management. Future research can expand upon these models by incorporating real-time data and exploring deep learning techniques to enhance predictive reliability further
Optimizing the viola-jones algorithm for robust face recognition in variable lighting and orientation conditions Gunawan, Gunawan; Aisyah, Nur; Santoso, Nugroho Adhi
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.v8i1.5220

Abstract

Facial recognition is a critical technology in digital security, driven by significant advances in computer vision. This research focuses on optimizing the Viola-Jones algorithm to improve the accuracy and speed of face detection by adjusting parameters and integrating more sophisticated image processing techniques. Facing challenges such as suboptimal lighting and variations in face orientation, the study adopted a rigorous experimental design, in-depth quantitative analysis, and robust model validation. Of the ten facial images collected, all were intensively processed using Haar-like features to identify significant patterns and adjust algorithm parameters in Python. This optimization process increased performance from 7 identified faces to 9 post-optimization identified faces and a substantial decrease in detection time from 0.0065 seconds to 0.0017 seconds per image. The comprehensive evaluation showed an increase in accuracy from 70% to 90%, recall from 70.0% to 90.0%, Precision remained constant at 100.0%, and F1-score from 82.35% to 94.74%. These results show that the optimization has increased the algorithm's sensitivity to changes in light intensity and face orientation and improved the effectiveness of facial recognition systems in complex and dynamic security scenarios while providing concrete evidence of the benefits of using Haar-like features in the Viola-Jones algorithm
Application of ant colony algorithm to optimize waste transport distribution routes in Tegal Gunawan, Gunawan; Handayani, Sri; Anandianskha, 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.v8i1.5223

Abstract

Effective and efficient waste management is an essential challenge in developing cities like Tegal City. Optimizing waste transport routes can reduce operational costs and environmental impact. This study aims to implement the Ant Colony Algorithm (ACO) to optimize waste distribution routes in Tegal City. This method was chosen for its proven ability to solve route optimization problems. This study developed a model for the simulation and analysis of waste transportation routes using actual location data from the Integrated Waste Treatment Site (TPST) to the landfill (TPA). The results showed that the implementation of ACO reduced the total mileage from 27.50 km to 21.05 km, a significant reduction that shows the algorithm's efficiency in determining the optimal route. The conclusion of this study confirms that ACO can be effectively used to improve waste transportation operations
Application of artificial neural network with optimization of genetic algorithms for weather prediction Gunawan, Gunawan; Miftakhudin, Muhammad; 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.v8i1.5225

Abstract

This research integrates Artificial Neural Network (ANN) with Genetic Algorithm Optimization (GA) to improve the accuracy of weather prediction. This method utilizes ANN-optimized GA, creating a model that can adapt to the dynamics of weather patterns. Using a dataset that includes meteorological variables such as temperature, humidity, and precipitation from January 1, 2023, to October 28, 2023, the model was tested for its ability to predict weather conditions accurately. The process begins with data preprocessing, ANN training, and GA optimisation. The evaluation showed that the optimized model was able to reduce the Mean Absolute Error (MAE) from 1.6865 to 0.8701, the Mean Absolute Percentage Error (MAPE) from 5.9864 to 3.1408, and the Root Mean Squared Error (RMSE) from 2.253 to 1.039, signalling a significant improvement in prediction accuracy and efficiency. This research confirms the potential of ANN and GA integration in improving weather prediction, providing new insights for developing more accurate and reliable prediction models for various applications, from agriculture to disaster management.
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
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 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 Limaknun, Lulu Lutfayza, Rezi Maulana, M Taufik Fajar Miftakhuddin, Ahmad Miftakhudin, Muhammad Mohamad Rifki Septiadi Mohammad Amin Triwinanto Triwinanto 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 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 Surur, Misbahu Sya’bani, Adhita Zulfa Syefudin, Syefudin Ubaidillah, Muhamad Rizal Ujianto, Nur Tulus W.N, Naella Nabila Putri Wahyu Pratama, Raka Wahyuning Naja, Naella Nabila Putri Wilda Shabrina Windi Setiawati Wresti Andriani Wresti Andriani Wresti Andriani Yan Kurniawan Yan Kurniawan, Yan Yulison Herry Chrisnanto Zaenul Arif Zain Hidayatullah, Fikri Zain, Ahmad Muzakky