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Application of genetic algorithm and backpropagation neural networks to predict Tegal City population Murtopo, Aang Alim; Nursahid, Wahyu; Fadilah, Nurul; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.308

Abstract

Use of Genetic Algorithms and Backpropagation Neural Networks for Population Prediction in Tegal City, which aims to create precise prediction models using advanced computational techniques. This research uses a quantitative approach that combines experimental methods, data analysis, and model validation to implement and test predictive models. By using genetic algorithms for parameter optimization and neural network backpropagation for training, the findings show that the model can accurately predict population numbers with minimal error and high determination coefficients. The implications of this study are significant for urban planning and public policy development due to the accuracy and effectiveness of the model in forecasting population growth based on historical data.
Development of mobile applications for IoT-based room temperature monitoring and control Murtopo, Aang Alim; Amalani, Mukhamad Zulfa Bakhtiar; Syefudin, Syefudin; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.309

Abstract

The Internet of Things (IoT) has become one of the most significant technologies, offering a wide range of innovative solutions to improve efficiency and convenience in various aspects of life. One important application of IoT is in environmental management and control, especially room temperature. This research aims to develop a mobile application capable of monitoring and controlling room temperature with an easy-to-understand user interface and the ability to forecast future temperature needs. Research methods used include experimental approaches, data analysis, and model validation to ensure applications function optimally in real-world conditions. The results showed that the application developed was effective in monitoring room temperature conditions in real-time and was able to adjust the temperature quickly and accurately. The implication of this research is the improvement of user convenience and energy efficiency through the use of IoT technology in everyday life.
Applying certainty factor method to identify diseases in rice plants Nugroho, Bangkit Indarmawan; Miftakhuddin, Ahmad; Syefudin, Syefudin; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.310

Abstract

Rice (Oryza Sativa L) is the most important food crop in the world after wheat and corn, as well as the main source of protein for most of the world's population, especially in Asia. The Save Swamps for Prosperous Farmers (Serasi) program in Central Java Territory cannot run well considering the tall capacity of existing rice agriculturists to bargain with bugs and maladies of the rice they plant, so it is essential to make a device within the frame of an master framework for diagnosing rice plant infections.  For this reason, it is very important to be aware of the factors that influence production levels. Disease is one of the most detrimental factors in rice production, where many losses are caused by disease. Each of these diseases generally shows symptoms of the disease suffered before it reaches a more severe and widespread stage, these symptoms can be recognized by carrying out a diagnosis first. This can be done using an expert system. In this research, an expert system was utilized which was made utilizing the certainty figure strategy, with a test of 25 ranchers within the West Tegal Area, Tegal City. From the comes about of the inquire about carried out, it was concluded that with this framework the level of exactness obtained using the posttest contains a esteem of 100%, in other words the framework encompasses a decently tall level of accuracy.
Implementation of blockchain technology in digital financial management systems Murtopo, Aang Alim; Anshori, Abu Hasan Al; Santoso, Nugroho Adi; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.314

Abstract

This research aims to develop and test a digital financial management system model that is integrated with blockchain technology to address security, transparency, and efficiency issues in the traditional digital financial system. Blockchain technology is used to ensure the integrity and security of data by recording each transaction in the form of interlinked and immutable blocks. The methods used include experimental approaches, quantitative analysis, and model validation. The results of the study show that blockchain integration improves the transparency, security, and operational efficiency of digital financial management systems. Although the designed asset recording application still has weaknesses in UX and UI, such as the lack of drop-down features and manual data entry, blockchain technology has successfully strengthened data security with the use of unique record IDs (hashes) that cannot be changed and public transparency through Etherscan. This research makes a practical contribution to the application of blockchain technology in the financial industry and suggests further development to improve the user experience and add features that improve the efficiency and flexibility of the asset recording system. These findings support the potential of blockchain in advancing the integrity and performance of the digital financial system.
Optimization Selection on Deep Learning Algorithm for Stock Price Prediction in Indonesia Companies Gunawan, Gunawan; Andriani, Wresti; Anandianskha, Sawaviyya; Murtopo, Aang Alim; Nugroho, Bangkit Indarmawan; Naja, Naella Nabila Putri Wahyuning
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.47935

Abstract

Purpose: Share price movements after the COVID-19 pandemic experienced a decline in several sectors, especially in the share prices of the Aneka Tambang Company, which operates in the mining sector, the Wijaya Karya Company in the construction sector, and the Sinar Mas Company, which is a Holding Company. Several factors influence this, including investors' hesitation in investing their money. This research aims to predict stock price movements using a Deep Learning algorithm, which is optimized using Selection optimization at three large companies in Indonesia, namely PT. ANTAM, PT. WIKA, and PT. SINAR MAS. So that it can provide the correct information to investors to avoid losses.Method: research through collecting data from the three companies, preprocessing, and then analyzing research data with several alternatives. The combination of inputs from the three companies using the deep learning method is then optimized using selection optimization to produce the best accuracy and use the results of the RMSE evaluation.Results: The results of this research show that by using the Deep Learning method, the best evaluation results were obtained for the Company PT Wijaya Karya with an RMSE value of 0.432, an MAE value of 0.31505 and an MSE value of 1913.953. These results were then optimized using Selection optimization to obtain an RMSE increase of 0.022, namely 0.410.Novelty: The contribution of this research is to get the best combination of input variables obtained using the windowing process from the three companies, which are then processed using the Deep Learning method to produce the most accurate evaluation results from the three companies, then the results are optimized again using Selection optimization to get the more optimal results.
Implementasi Algoritma Greedy untuk Optimasi Rute Layanan Logistik UMKM di Kota Tegal Andriani, Wresti; Gunawan, Gunawan; W.N, Naella Nabila Putri
Jurnal Teknologi Vol. 13 No. 1 (2025): Jurnal Teknologi
Publisher : Universitas Jayabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31479/jtek.v13i1.415

Abstract

Permasalahan efisiensi rute pengiriman masih menjadi hambatan utama layanan logistik UMKM di Kota Tegal. Penelitian ini bertujuan mengimplementasikan algoritma Greedy nearest neighbor untuk menyusun rute pengiriman yang lebih efisien dibandingkan rute manual kurir. Metode yang digunakan adalah eksperimen kuantitatif berbasis distance matrix dari Google Distance Matrix API pada studi kasus 10 titik dan skenario perluasan hingga 30–50 titik. Algoritma diimplementasikan dengan Python dan dievaluasi menggunakan metrik jarak tempuh, waktu tempuh, persentase penghematan, serta simpangan baku dan interval kepercayaan 95%. Hasil pengujian menunjukkan bahwa pada 10 titik, rute manual menempuh sekitar 46,05 km (±92,10 menit), sedangkan rute Greedy hanya 25,91 km (±51,82 menit) dengan penghematan jarak dan waktu sekitar 43,74%. Pada skenario 30 dan 50 titik, jarak berkurang sekitar 35–36% dengan waktu komputasi di bawah 1 detik. Temuan ini mengindikasikan algoritma Greedy nearest neighbor layak dijadikan fondasi sistem optimasi rute logistik UMKM berbasis data. Keywords: Delivery route planning, Google Distance Matrix API, Greedy nearest neighbor, MSME logistics, Route optimization.
Sistem Informasi Berbasis Web untuk Pendaftaran Kompetisi Internasional Didiek Trisatya; Priyo Haryoko; Gunawan Gunawan; Nur Tulus Ujianto
JESII: Journal of Elektronik Sistem InformasI Vol 3 No 2 (2025): Journal of Elektronik Sistem InformasI - JESII (DECEMBER)
Publisher : Departement Information Systems Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/jesii.v3i2.4409

Abstract

The management of international student competitions in higher education institutions often faces challenges related to fragmented registration processes, manual data handling, and inefficient submission management. These issues may lead to data inconsistency, administrative errors, and limited transparency for participants. This research aims to design and implement a web-based information system that integrates competition registration and work submission into a single platform. The study adopts a system development–oriented approach, including requirement analysis, system design, implementation, and functional testing. The proposed system supports an integrated registration and submission process for participants, role-based access for administrators, administrative verification, and real-time status monitoring. The implementation results indicate that the system operates according to functional requirements and improves administrative efficiency, data accuracy, and accessibility for users. By centralizing registration and submission processes, the system reduces redundancy and simplifies competition management workflows. This research provides a practical solution for managing international student competitions, with a case implementation at Universitas Pancasakti Tegal.
Prediksi Harga Cabai Musiman Menggunakan Model LSTM di Jawa Tengah Gunawan, Gunawan; Andriani, Wresti; Ujianto, Nur Tulus
INFOMATEK Vol 27 No 2 (2025): Desember 2025
Publisher : Fakultas Teknik, Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/infomatek.v27i2.26460

Abstract

Penelitian ini mengembangkan model prediksi harga cabai musiman di Jawa Tengah menggunakan algoritma Long Short-Term Memory (LSTM) yang mengintegrasikan data harga harian dan data cuaca, termasuk curah hujan, suhu udara, dan kelembapan relatif. Model ini dirancang untuk memprediksi fluktuasi harga cabai yang dipengaruhi oleh faktor musiman dan kondisi cuaca, yang sering menyebabkan ketidakstabilan harga. Hasil eksperimen menunjukkan bahwa model LSTM berhasil menghasilkan prediksi dengan nilai RMSE 512,83, MAE 387,49, dan R² 0,861, yang mengindikasikan kemampuan model dalam menangkap pola harga yang dipengaruhi oleh faktor eksternal. Keunggulan utama LSTM dibandingkan dengan model lain seperti Support Vector Regression (SVR) dan Random Forest terletak pada kemampuannya untuk menangkap korelasi temporal dan pola musiman dalam data deret waktu. Implikasi praktis dari penelitian ini meliputi penggunaan model untuk membantu petani dalam menentukan waktu tanam dan panen yang optimal serta bagi pemerintah daerah dalam mengatur distribusi dan pengendalian harga cabai untuk mengurangi dampak inflasi pangan dan meningkatkan ketahanan pangan. Penelitian ini membuka peluang untuk penelitian lanjutan yang dapat mengembangkan model yang lebih kompleks dan mengakomodasi faktor eksternal lainnya.
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