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All Journal JELTII (Journal of English Language Teaching and Islamic Integration) Jurnal Ilmu Komputer dan Desain Komunikasi Visual Jurnal Teknologi Dan Sistem Informasi Bisnis Informatics, Electrical and Electronics Engineering International Journal Of Computer, Network Security and Information System (IJCONSIST) Jurnal Iqtisaduna Journal Of Informatics And Busisnes Nusantara Journal of Computers and its Applications ITIJ Submit : Jurnal Ilmiah Teknologi Informasi dan Sains Nusantara Computer and Design Review
Angga Lisdiyanto
Universitas Nahdlatul Ulama Sidoarjo
Religion Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Other

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Pengembangan Sistem Pemetaan Masjid Berbasis Web Geographic Information System (GIS) Galih Muhamad Rachman; Angga Lisdiyanto; Anggay Luri Pramana
Nusantara Computer and Design Review Vol. 2 No. 1 (2024): Nusantara Computer and Design Review
Publisher : LPPM UNUSIDA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55732/ncdr.v2i1.1293

Abstract

Masjid berfungsi sebagai tempat ibadah sholat dan pusat kesejahteraan umat, sehingga diperlukan sistem pemetaan berbasis Web Geographic Information System (GIS) untuk mengetahui persebaran masjid di Kabupaten Sidoarjo yang mayoritas penduduknya beridentitas Nahdlatul Ulama. Peneletian ini menggunakan data spasial berupa titik koordinat lokasi masjid. Penelitian ini juga menggunakan data non spasial seperti nama masjid, alamat masjid, deskripsi masjid dan foto masjid. Pembuatan WebGIS pemetaan masjid yang berada di bawah naungan PCNU Sidoarjo ini menggunakan Leaflet Map sebagai peta dasar, menggunakan MySQL sebagai manajemen database dan menggunakan Framework Laravel yang memiliki komponen bahasa pemrograman berupa PHP, HTML, CSS, dan JavaScript yang digunakan untuk pengembangan Web dari sisi Front-end dan Back-end. Penelitian ini menghasilkan sistem pemetaan masjid berbasis WebGIS untuk PCNU Sidoarjo yang menyajikan lokasi, data masjid, fitur pencarian, dan penambahan data baru. Mosques function as places of prayer and welfare centres for the people, so a Web Geographic Information System (GIS) based mapping system is needed to determine the distribution of mosques in Sidoarjo Regency, where most of the population has the Nahdlatul Ulama identity. This research uses spatial data in the form of coordinates of mosque locations. This research also uses non-spatial data such as mosque names, addresses, descriptions, and photos. Creating a Web GIS for mapping mosques under the auspices of PCNU Sidoarjo uses Leaflet Map as a base map, MySQL as database management and the Laravel Framework, which has programming language components in the form of PHP, HTML, CSS, and JavaScript, which are used for Web development from the side. Front-end and Back-end. This research produces a Web GIS-based Mosque mapping system for PCNU Sidoarjo, which provides locations, mosque data, search features and the addition of new data.
EDUCATIONAL STRATEGIES FOR IMPROVING LISTENING COMPREHENSION IN LANGUAGE EDUCATION Purnomo, Ryan; Ganal Arief Rahmawan; Achmad Mufliq; Ahmad Wahyudi; Pratama Wirya Atmaja; Angga Lisdiyanto; Tri Septianto
Journal of English Language Teaching and Islamic Integration Vol. 8 No. 02 (2025): Journal of English Language Teaching and Islamic integration
Publisher : STKIP AL HIKMAH SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62426/

Abstract

Pemahaman mendengarkan sering kali disalahartikan sebagai aktivitas pasif, padahal sebenarnya merupakan proses yang dinamis dan membutuhkan keterlibatan kognitif yang tinggi. Berbagai penelitian menekankan pentingnya strategi kognitif dan metakognitif dalam meningkatkan keterampilan menyimak. Penelitian mengenai kemandirian belajar menunjukkan bahwa pembelajar yang berhasil adalah mereka yang secara aktif mengatur proses belajarnya, memantau kemajuan, serta memanfaatkan umpan balik dan sumber daya yang tersedia secara efektif. Penelitian ini menggunakan pendekatan metode campuran (mixed-methods) dengan melibatkan 22 siswa yang dipilih secara acak dari kelas XI TKR di SMK Pasundan Subang. Pengumpulan data dilakukan melalui kombinasi pre-test dan post-test, kuesioner terstruktur, wawancara semi-terstruktur, serta observasi kelas. Analisis data dilakukan dengan metode kuantitatif, termasuk uji t berpasangan (paired t-test) dan statistik deskriptif, serta analisis kualitatif melalui pendekatan tematik terhadap hasil wawancara dan observasi. Artikel ini membahas penerapan strategi kognitif dan metakognitif dalam pengajaran keterampilan menyimak bahasa kedua. Penulis berpendapat bahwa pendidik dapat membantu siswa menjadi pendengar yang lebih terampil dengan membekali mereka strategi seperti pengulangan, membuat ringkasan, dan bertanya pada diri sendiri. Teknik-teknik ini tidak hanya meningkatkan pemahaman, tetapi juga mendorong kemandirian belajar, di mana siswa lebih bertanggung jawab terhadap proses belajarnya. Pada akhirnya, strategi-strategi ini berkontribusi terhadap keterlibatan yang lebih bermakna dengan materi pembelajaran dan mendukung retensi serta penerapan keterampilan bahasa dalam berbagai konteks kehidupan nyata.
Stock Price Prediction Using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) Methods Riza Akhsani Setyo Prayoga; Ariansyah, Fery Almas; Daffa, Muhammad Falikhuddin; Laqma Dica Fitrani; Masti Fatchiyah Maharani; Angga Lisdiyanto; Angkawidjaja , Steven
IJCONSIST JOURNALS Vol 7 No 1 (2025): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i1.158

Abstract

This research aims to improve the accuracy of stock price prediction through the application of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) methods, focusing on stocks from the Composite Stock Price Index (CSPI) referred to as the IDX Composite. The research process includes comprehensive steps, including data collection and preprocessing, dataset creation with emphasis on stock closing prices, and division of the dataset into training and test data. The LSTM and GRU models were designed with a recurrent layer and a Dense layer and then trained for 100 epochs with a batch size of 32. Model evaluation was performed by comparing key metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE) on the test set. The EPOCH-RMSE graph provides an overview of the changes in the RMSE value during training. The best result of the LSTM model was achieved at the 96th epoch with RMSE 40.36, MSE 1385.97, and MAE 30.09, while GRU achieved peak performance at the 92nd epoch with RMSE 37.33, MSE 908.29, and MAE 25.42. In conclusion, GRU can be considered as a more effective option in predicting JCI stock prices based on performance evaluation using various metrics such as RMSE, MSE, and MAE.
3D S-UNET an Efficient Architecture for 3 Dimensional Segmentation of Brain Tumors on MRI Images Wibowo, M Sadewa Wicaksana; Muhammad Shodiq; Bety Qorry Aina; Angga Lisdiyanto
Information Technology International Journal Vol. 3 No. 2 (2025): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v3i2.53

Abstract

One of the deadliest diseases worldwide is brain tumors. In identifying brain tumors, experts perform a subjective analysis that requires considerable time. Previous research has developed automatic 3D brain tumor segmentation using Deep Learning (DL) approaches such as 3D UNet and 3D ResNet. However, these approaches demand significant computational resources. In resource-constrained settings, key criteria for determining the best architecture include memory consumption, inference speed, and accuracy. Therefore, this study introduces the development of the 3D S-UNet architecture, constructed by combining 3D ShuffleNet-V2 as an encoder and 3D UNet as a decoder. The integration of these 3D data processors allows the architecture to be more precise in identifying brain tumor locations and capture richer feature values compared to 2D data processing. The researchers compare 3D S-UNet with another Lightweight Deep Learning architecture, 3D Mobile-UNet. The results show that 3D S-UNet has a smaller memory consumption, using 0.56GB for the highest allocated memory and 1.71GB for reserved memory. In terms of inference speed, 3D S-UNet is faster compared to the other three architectures, achieving a speed of 135.881 milliseconds. 3D S-UNet demonstrates favorable results with a Whole Tumor (WT) dice score, sensitivity, and specificity of 83%, 85%, and 88%, respectively.
GPS-Based Digital Business Technology Innovation in Community Health Centers: Application Development for Health Staff Performance Management Fitrani, Laqma; Angga Lisdiyanto
Information Technology International Journal Vol. 3 No. 2 (2025): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

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

Abstract

Attendance management in community health centers (Puskesmas) often relies on manual procedures that are prone to inaccuracy, limited traceability, and weak verification mechanisms, particularly for staff performing field-based duties. These limitations hinder the effectiveness of performance monitoring and reduce administrative efficiency. This study aims to develop a GPS-based digital attendance system designed to enhance accuracy, accountability, and transparency in monitoring employee presence at Puskesmas Mantup. The research methodology comprises four stages: Observation to identify operational constraints; Planning and Analysis to formulate functional and non-functional requirements; System Design to model data structures, user interfaces, and workflow diagrams; and Implementation to develop the application using web technologies integrated with geolocation services. System functionality was validated through Blackbox Testing to ensure reliability across key processes, including login authentication, location validation, shift scheduling, and automated recording of attendance events. The results indicate that the system successfully performs real-time GPS verification, prevents false check-ins outside the designated radius, and supports both shift and non-shift attendance schemes. Additionally, the dashboard and reporting features provide comprehensive visibility for administrators in evaluating employee performance. Overall, the GPS-based attendance system substantially improves monitoring accuracy and operational efficiency, offering a scalable solution for adoption in primary healthcare settings.
Co-Authors Achmad Mufliq Aflachul Mu’minin Agus Setia Budi Ahmad Wahyudi Anggay Luri Pramana Angkawidjaja , Steven Arda Surya Editya Ariansyah, Fery Almas Arief Saputro Bety Qorry Aina Catur Lega Wibisono Cholilurrohman Daffa, Muhammad Falikhuddin Fitrani, Laqma Galih Muhamad Rachman Ganal Arief Rahmawan Lailatul Musyaffaah Laqma Dica Fitrani Masti Fatchiyah Maharani Moch. Fahrul Orizky Muhammad Muharrom Al Haromainy Muhammad Shodiq Neny Kurniati Pratama Wirya Atmaja Purnomo, Ryan Riza Akhsani Setyo Prayoga Rizky Aditya Nugroho Syahri Mu’min Tri Septianto Wahyu Eko Pujianto Wibawa, Koerniawan Dwi Wibowo, M Sadewa Wicaksana Winarti Winarti Winarti