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All Journal JITEK (Jurnal Ilmiah Teknosains) IJCIT (Indonesian Journal on Computer and Information Technology) Indonesian Journal on Software Engineering (IJSE) JIEET (Journal of Information Engineering and Educational Technology) InComTech: Jurnal Telekomunikasi dan Komputer Jurnal ULTIMA Computing JURIKOM (Jurnal Riset Komputer) KOMPUTEK Majalah Ilmiah UNIKOM Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Infortech Computer Science (CO-SCIENCE) IMTechno: Journal of Industrial Management and Technology Simpatik: Jurnal sistem Informasi dan Informatika JUSTIN (Jurnal Sistem dan Teknologi Informasi) International Journal of Advanced Science Computing and Engineering Journal Medical Informatics Technology Jurnal Informatika: Jurnal Pengembangan IT Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Waeisul Bismi
Universitas Bina Sarana Informatika
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Menerapkan Algoritma Djikstra dan Metode Heuristic Dalam Menentukan Jalur Terpendek Menuju Kampus Noor, Mohamad; Bismi, Waeisul; Gata, Windu
IJCIT (Indonesian Journal on Computer and Information Technology) Vol 6, No 2 (2021): IJCIT November 2021
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (430.18 KB) | DOI: 10.31294/ijcit.v6i2.9887

Abstract

AbstrakKemacetan adalah situasi atau keadaan tersendatnya atau bahkan terhentinya lalu lintas yang disebabkan oleh banyaknya jumlah kendaraan yang melebihi kapasitas jalan. Kemacetan banyak terjadi di kota-kota besar, terutama kota yang tidak mempunyai transportasi publik yang baik atau kurang memadai. Bisa juga tidak seimbangnya kebutuhan jalan dengan kepadatan penduduk. Banyak langkah-langkah yang sudah dilakukan oleh pemerintah untuk mengatasi kemacetan. Akan tetapi, kemacetan tetap saja masih terjadi. Oleh karena itu pengguna jalan harus menemukan cara sendiri untuk mengatasi masalah tersebut. Salah satu cara yang efektif yaitu mencari jalur alternatif terpendek yang dapat dilalui dengan menggunakan Algoritma Dijkstra. Penggunaan Algoritma Dijkstra dan metode Heuristic dapat memberikan peluang solusi, karena Algoritma Dijkstra yang dipaduakan dengan metode Heuristic ini memberikan keluaran berupa jalur tercepat dan terpendek dari tempat asal menuju tempat tujuan. Dalam penelitian ini peneliti mendapati jalur terpendek dari wilayah bojong kulur menuju tempat tujuan yaitu kampus Universitas Nusa Mandiri dengan menerapkan  Algortitma Djikstra  dan metode Heuristic yakni dengan jarak 26,1 Km.Kata Kunci: algoritma dijkstra, Heuristic, jalur terpendekAbstractCongestion is a situation or condition of traffic jams or cessation of traffic caused by the large number of vehicles that exceed the capacity of the road. Congestion occurs a lot in big cities, especially cities that do not have good or inadequate public transportation. It could also be that the road needs are not balanced with the population density. Many steps have been taken by the government to overcome congestion. However, traffic jams still occur. Therefore, road users must find their own way to solve the problem. One effective way is to find the shortest alternative path that can be traversed using Dijkstra's Algorithm. The use of Dijkstra's Algorithm and Heuristic methods can provide solution opportunities, because Dijkstra's Algorithm combined with this Heuristic method provides output in the form of the fastest and shortest path from the origin to the destination. In this study, researchers found the shortest path from the bojong kulur area to places, namely the Nusa Mandiri University campus by applying the Djikstra algorithm and Heuristic methods, with a distance of 26.1 Km. Keywords: dijkstra algorithm, Heuristics, shortest path
Prediksi Harga Saham Netflix Inc. (NFLX) di Nasdaq menggunakan Algoritma Machine Learning berbasis Data Mining Bismi, Waeisul; Siregar, Aris Parnius; Qomaruddin, Muhammmad
KOMPUTEK Vol. 9 No. 2 (2025): Oktober
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/jkt.v9i2.3452

Abstract

Pergerakan harga saham yang fluktuatif membuat prediksi harga saham menjadi aspek penting dalam pengambilan keputusan investasi. Netflix Inc. (NFLX), sebagai salah satu emiten teknologi terkemuka yang terdaftar di NASDAQ, menunjukkan pola pergerakan harga saham yang dinamis dan dipengaruhi oleh berbagai faktor internal dan eksternal. Penelitian ini bertujuan untuk menerapkan algoritma machine learning berbasis data mining dalam memprediksi harga saham Netflix Inc., dengan menggunakan perangkat lunak Orange Data Mining yang bersifat visual dan mendukung berbagai metode klasifikasi. Metode penelitian terdiri dari lima tahapan utama, yaitu: pengumpulan data, pra-pemrosesan data, implementasi model, pengujian model, dan evaluasi kinerja. Dataset yang digunakan diperoleh dari Investing.com dalam periode Januari 2022 hingga Desember 2023, dengan total 501 data harian yang mencakup fitur seperti harga pembukaan, harga penutupan, harga tertinggi, harga terendah, dan volume transaksi. Algoritma klasifikasi yang digunakan meliputi Support Vector Machine (SVM), Neural Network, dan Naive Bayes. Data dibagi menjadi 80% data pelatihan dan 20% data pengujian. Hasil pengujian menunjukkan bahwa algoritma Neural Network memberikan performa terbaik dengan nilai AUC sebesar 0,937, akurasi (CA) 88%, dan nilai MCC sebesar 0,759. Algoritma SVM menghasilkan akurasi 84,3% dan MCC 0,690, sedangkan Naive Bayes memiliki akurasi 83% dengan MCC 0,660. Evaluasi menggunakan confusion matrix memperlihatkan bahwa model mampu mengenali pola klasifikasi naik atau turun dengan tingkat kesalahan yang minim. Dengan demikian, penerapan algoritma machine learning pada data mining terbukti efektif dalam melakukan prediksi harga saham, dan dapat digunakan sebagai acuan dalam pengambilan keputusan investasi di pasar modal berbasis data historis.
SISTEM INFORMASI PANGGIL KULI PROYEK (SIPAKUPROY) BERBASIS WEBSITE MENGGUNAKAN FRAMEWORK CODEIGNITER Bismi, Waeisul; Febriyani, Anisa; Ramadhan, Farid
(JITEK)Jurnal Ilmiah Teknosains Vol 9, No 2/Nov (2023): JiTek
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/jitek.v9i2/Nov.16198

Abstract

The development of Website-based information systems has provided significant benefits in terms of distance, time, and accessibility, particularly in the construction industry for finding skilled and experienced construction workers. However, challenges arise in finding qualified workforce due to the phenomenon of globalization. To overcome this issue, SIPAKUPROY is designed as an information system that supports these needs. Through SIPAKUPROY, users can access multiple construction worker teams, obtain detailed pricing information, and follow an efficient payment process. The transition process from manual to digital sales systems, known as digitalization, is a crucial key in implementing SIPAKUPROY, which requires suitable and reliable software and hardware. In the design of SIPAKUPROY, user-friendly design is the main focus, aiming to provide an easy and comfortable user experience. Overall, SIPAKUPROY offers a solution for individuals seeking reliable construction workers without personal connections in the industry. Thus, SIPAKUPROY makes a positive contribution in addressing the challenges faced in finding trustworthy construction workers in this era of globalization.
Pengembangan Aplikasi Prediksi Harga Emas Berbasis Web Menggunakan Model Time Series Abdullah, Fikrian Nur; Nurardian, Ridwana Septian; Liya, Amel; Saputra, Ari Setia; Saputra, Atio Wahyudi; Bismi, Waeisul
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.9165

Abstract

High gold price volatility due to global economic instability poses challenges in investment decision-making. This research aims to develop a web-based gold price prediction application using a time series model, focusing on the Gated Recurrent Unit (GRU) algorithm. This application is designed to present real-time, accurate, and easily accessible gold price predictions, thereby increasing the efficiency and transparency of information for investment decision making. The development process starts from collecting and preprocessing daily gold price data for the period 2013-2023, then comparing four predictive models: LSTM, GRU, ARIMA, and XGBoost. Evaluation is performed using MAE, RMSE, and R² metrics. Results showed that GRU provided the best performance with an RMSE value of 17.76 and R² of 0.9410. The GRU model is integrated into a web application using the Flask framework, with an interactive HTML-based interface and Chart.js visualization. This application presents real-time gold price predictions and can be accessed by general users and investors. The results of this study show that the time series approach with GRU is effective in projecting gold prices, and can be a relevant tool in supporting data-based investment decisions.
Co-Authors Abdullah, Fikrian Nur Ade Setiawan Ade Setiawan Anisa Febriyani Anton . Arina Selawati Deny Novianti Dicky Hariyanto Dwiza Riana Dwiza Riana Fajar Shidiq Farid Ramadhan Febriyani, Anisa Firmansyah Firmansyah Gata, Windu Hani Harafani Hewiz , Alya Shafira Jordy Lasmana Putra Jufriadif Na`am, Jufriadif Liya, Amel Maysaroh, Maysaroh Mugi Raharjo muhammad qomaruddin Muhammad Qomaruddin Musriatun Napiah Noor, Mohamad Nurardian, Ridwana Septian Nurbaety Nurbaety Putri, Destiana Putri, Halimmatussa’diyah Putri, Halimmatussa’diyah Qomaruddin, Muhammmad Rachmat Adi Purnama Rachmawati Darma Astuti Rachmawati Darma Astuti Raharjo, Mugi Ramadhan, Farid Rangga Wardhana Sakata, Yama Sulkhan Sandra Dewi Saraswati Saputra, Ari Setia Saputra, Atio Wahyudi Siregar, Aris Parnius Taufik Asra Tommi Alfian Armawan Sandi Wijaya, Aryanata