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All Journal International Journal of Electrical and Computer Engineering Jurnal Informatika dan Teknik Elektro Terapan JOIN (Jurnal Online Informatika) JURNAL LENTERA : Kajian Keagamaan, Keilmuan dan Teknologi JURNAL PENDIDIKAN TAMBUSAI J-SAKTI (Jurnal Sains Komputer dan Informatika) KOMPUTEK Jurnal Teknologi Terpadu JOURNAL INFORMATICS, SCIENCE & TECHNOLOGY Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal HIDROPILAR Idealis : Indonesia Journal Information System Jurnal Ilmiah Wahana Pendidikan J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Sosial dan Abdimas Jurnal Ilmiah Pendidik Indonesia Jurnal Informatika Polinema (JIP) Journal of Informatics and Information Security Teknologi Informasi dan Digital (TRIDI)
Carudin, Carudin
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Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences Other

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Prediksi Harga Saham Sektor Energi Menggunakan Algoritma Long Short-Term Memory (LSTM) Alfatah, Addin Firdaus; Sari, Betha Nurina; Carudin, Carudin
Jurnal Ilmiah Wahana Pendidikan Vol 12 No 2.D (2026): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

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Abstract

Stock prices are time series data that are highly volatile and difficult to predict accurately due to the influence of various internal and external factors. To address this issue, a model is needed that can learn historical patterns and capture long-term dependencies in the data. This study adopts the Knowledge Discovery in Database (KDD) methodology, which consists of five main stages: data selection, data cleaning, transformation, data mining, and evaluation. The dataset used consists of historical stock price data from the energy sector over a five-year period (2019–2023), covering five companies: ELSA, AKRA, INDY, MEDC, and PGAS. The LSTM model was trained using a batch size of 32 and 20 epochs, with an 80:20 train-test data split. Model evaluation was conducted using two main metrics: Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Among the five stocks analyzed, the best results were achieved on ELSA with an RMSE of 12.21 and MAPE of 2.61%, indicating a very high level of prediction accuracy. In conclusion, the LSTM algorithm is capable of predicting stock prices in the energy sector effectively, particularly in recognizing medium-term trends. Although the model still has limitations in capturing sharp price fluctuations, the overall results demonstrate that LSTM is an effective method for stock price prediction based on historical data.
MONITORING GAS BERBAHAYA KARBON MONOKSIDA (CO) PADA RUANG KERJA MENGGUNAKAN SENSOR MQ 135 DAN LED MATRIX P10 SEBAGAI TAMPILAN Carudin; Marisa; Eka Sarah Dewi, Belinda; Chodijah, Siti
Jurnal Teknologi Informasi dan Digital Vol. 3 No. 1 (2025): Teknologi Informasi dan Digital
Publisher : LPPM Universitas Bani Saleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65624/tridi.v3i1.70

Abstract

Perkembangan teknologi dan pembangunan yang pesat, memberikan dampak negatif yaitu mengakibatkan kualitas udara semakin menurun akibat terkontaminasi oleh polutan yang berasal dari aktivitas pembakaran sampah, gas buang kendaraan bermotor, dan kegiatan industri yang menghasilkan polusi. Pekerja atau karyawan kantoran seringkali lalai dalam memperhatikan kualitas udara dalam ruang kerjanya hal yang bisa menurunkan kualitas udara dalam ruangan adalah hasil dari AC dan atau gas sisa pembakaan lainya sehingga menyebabkan kerugian bagi manusia. Selain itu, gas-gas tersebut juga mengakibatkan terjadinya efek rumah kaca. Untuk mengatasi masalah tersebut, dirancanglah sebuah sistem monitoring gas berbahaya karbon monoksida menggunakan protokol TCP/IP untuk mengirimkan data ke platform IoT Ganesha16. Dalam input sistem digunakan sensor MQ-135 yang dapat mendeteksi metana. Data dari sensor akan dikirimkan ke Arduino, kemudian ditampilkan pada Led Matrix P10 sebagai Tampilan dan diteruskan melalui modul wifi ESP8266 agar dapat dikirimkan ke platform IoT Ganesha16. Data yang dikirimkan ke web server menggunakan protokol TCP/IP untuk kemudian ditampilkan dalam web server Ganesha16 agar data dapat ditampilkan secara realtime. Dalam eksekusi keseluruhan program yang dijalankan, didapatkan bahwa antara proses pertama (pembacaan sensor) sampai ke menampilkan data di Ganesha16 di dapatkan delay rata-rata 1,95 second. Delay pada bagian TCP/IP didapatkan rata-rata 1,37 second pada waktu realtime.
Analisis Keamanan Aplikasi Café Egg And Butter Berbasis Android Menggunakan Mobile Security Framework (MobSF) Fadillah, Adhitya Rizkyawan; Primajaya, Aji; Carudin, Carudin
Jurnal Ilmiah Wahana Pendidikan Vol 12 No 3.C (2026): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

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Abstract

Mobile application security is a crucial aspect of software development, particularly on the Android platform, which has a broad user base and an open-source nature. This study aims to analyze the security level of the Android-based Café EggAndButter application using the Static Application Security Testing (SAST) approach through the Mobile Security Framework (MobSF) tool. The main focus of this research is to determine the security score, identify potential vulnerabilities, and assess the application’s security risk level. The analysis result show that the application received a Security Score of 37/100 with Risk Rating Grade of C, indicating a relatively high security risk. Three major vulnerabilities were found: Exported Activity without access control, Insecure Logging, and unencrypted storage of login tokens. Additionally, the use of weak cryptographic algorithms (MD5 and SHA-1) and two requests for dangerous permissions were identified. However, no vulnerabilities related to SSL of malicious domains were found. In conclusion, the application still has several weaknesses that need to be addressed, especially in component management and data protection
Co-Authors Adeng Ichwan Maulida Agus, Khomisan Alfatah, Addin Firdaus Alfiah, Nur Rizqi Alpiyanto, Radi Ammar Hidayatulloh, Naufal Annaz, Fajar Apriningrum, Nelly Arga Adi Putra, Harvian Bintang, Aria Eka Sarah Dewi, Belinda Fadhillah, Octavia Salwa Dzaky Fadillah, Adhitya Rizkyawan Fahri Pahrudin, Adithia Firdiansyah, Adri Fitriani, Rina Galih Agustian Perdana Hapzi Ali Herjanto, Muhamad Fajar Yudhistira Heryana, Nono Hikmah Alim, Rizaldi Husain, Ibrohim Intan Purnamasari Iqbal Mubarok, Muhammad Jaman, Jajam Haerul Juwita, Ayu Ratna Khusurur Fauziah, Miftah Komarudin, Oman Kurniawan, Endro Sigit Marisa Marisa . Marisa Marisa Mayasari, Rini Mugiarso Negara, Adhi Kusuma Nur Ibrahim Nur Sallamah, Afifah Nurkarya, Yoyok Nurkifli, E. Haodudin Nurkifli Purwantoro Ramdani Ramdani . Rasim Rini Mayasari Rizki, Mohammad Salsabilla, Risma Auliya Saory, Farhan Satrio, Adi Septiadi, Michael Setiyadi, Johar Siti Chodijah Suhadi Suhadi Surya Gemilang, Wahyu Syahputra, Aditya Daffa Tri Susanto Umaidah, Yuyun Yusup, Dadang Yusup, Irvan Maulana Zulfikar, Dwi Aji