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All Journal Register: Jurnal Ilmiah Teknologi Sistem Informasi EMITTER International Journal of Engineering Technology G-Tech : Jurnal Teknologi Terapan Indonesian Community Journal
Darmawan, Zakha Maisat Eka
Politeknik Elektronika Negeri Surabaya
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Environmental Science Public Health Social Sciences Other

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Modified GTS Allocation Scheme for IEEE 802.15.4 Darmawan, Zakha Maisat Eka; Al Rasyid, M. Udin Harun; Sudarsono, Amang
EMITTER International Journal of Engineering Technology Vol 3, No 1 (2015)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (230.914 KB) | DOI: 10.24003/emitter.v3i1.92

Abstract

IEEE 802.15.4 standard is widely used in wireless personal area networks (WPANs). The devices transmit data during two periods: contention access period (CAP) by accessing the channel using CSMA/CA and contention free period (CFP), which consists of guaranteed time slots (GTS) allocated to individual devices by the personal area network (PAN). However, the use of GTS slot size may lead to severe bandwidth wastage if  the traffic pattern is not fit or only a small portion of GTS slot is used by allocated device. The proposed scheme devides the GTS slot and then optimizes the GTS slot size by exploiting the value of superframe order (SO) information. The proposed scheme was tested through simulations and the results show that the new GTS allocation scheme perform better than the original IEEE 802.15.4 standard in terms of average transmitted packets, throughput, latency and probability of successful packets.
Pengembangan Aplikasi Virtual Tour Taman Harmoni Sebagai Sarana Promosi Wisata di Kota Surabaya Zakha Maisat Eka Darmawan; Ashafidz Fauzan Dianta; Akhmad Alimudin; Aliv Faizal Muhammad; Dwi Susanto; Hestiasari Rante; Irma Wulandari; Kholid Fathoni; Moh Zikky; Muhammad Agus Zainuddin; Mifta Nauval Harizy; Rendra Suprobo Aji; Rosiyah Faradisa; Sritrusta Sukaridhoto; Tri Budi Santoso; Widi Sarinastiti
I-Com: Indonesian Community Journal Vol 4 No 3 (2024): I-Com: Indonesian Community Journal (September 2024)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/icom.v4i3.5104

Abstract

Pada era digital sekarang, pemanfaatan teknologi multimedia digunakan pada berbagai bidang, terutama pariwisata. Virtual tour yang merupakan bagian dari virtual reality hadir untuk memberi manfaat ke masyarakat untuk mendapatkan pengalaman baru dengan melakukan tur secara dunia maya ke tempat wisata tertentu. Kegiatan ini menyajikan proses dan hasil dari kegiatan pengabdian masyarakat yang bekerja sama dengan Pemerintah Kota Surabaya untuk mengembangkan aplikasi virtual tour Taman Harmoni berbasis website. Dengan lima tahapan pelaksanaan, dari observasi lapangan hingga dokumentasi, kami dapat mempublikasikan aplikasi tersebut dengan laman web yang bisa diakses oleh masyarakat dimanapun dan kapanpun. Sebanyak 71,4% pengunjung aplikasi yang telah mengeksplorasi fitur-fitur yang disediakan memberikan umpan balik yang baik terhadap keberadaan aplikasi ini.
Comparison of Machine Learning Classification Methods for Weather Prediction: A Performance Analysis Zakha Maisat Eka Darmawan; Ashafidz Fauzan Dianta; Kholid Fathoni; Oktavia Citra Resmi Rachmawati; Kevin Ilham Apriandy
G-Tech: Jurnal Teknologi Terapan Vol 9 No 2 (2025): G-Tech, Vol. 9 No. 2 April 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i2.6649

Abstract

Weather classification is crucial in various sectors, including agriculture, transportation, and disaster management. Accurate weather prediction can help mitigate risks and improve decision-making in these fields. However, classifying weather conditions remains challenging due to the complex and dynamic nature of meteorological data. This study aims to compare different machine learning classification methods to determine the most effective model for weather classification. The research employs a structured methodology consisting of seven key steps: literature study, data understanding, exploratory data analysis, data preparation, modeling, evaluation, and hyperparameter tuning. The study used Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Gradient Boosting, AdaBoost, and Extra Trees to identify the best-performing classifier. Model evaluation was conducted using accuracy, precision, recall, and F1-score. The results indicate that Gradient Boosting achieved the highest performance, surpassing other models with an accuracy of 90.15%. To optimize the model further, hyperparameter tuning was conducted using GridSearchCV, and feature selection was done using SelectKBest. This process resulted in an improved accuracy of 90.22%, demonstrating the effectiveness of model optimization.
Development of an Interactive Augmented Reality-Based Weather Forecast Simulation with Automated BMKG Data for Enhanced TV Broadcasting Moh. Zikky; Robby Alexander; Zakha Maisat Eka Darmawan; Fitrah Maharani Humaira; Laily Asna Safira
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 11 No 2 (2025): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v11i2.5117

Abstract

The weather has a significant impact on human activities. Weather data are used in various industries. Recently, Augmented Reality (AR) technology has been widely used to enhance content representation through multidimensional visualization. Television stations utilize AR to capture viewers' attention in the programs they broadcast; AR has the potential to enhance communication between viewers and the content. Our research on developing an AR application for an engaging immersive reality implementation aims to simulate a virtual stage in TV broadcasts, displaying real-time weather data from the Indonesian Meteorology, Climatology, and Geophysics Agency (BMKG) using the latest SQL database. The implementation of the AR system as a multimedia mode in delivering weather information increases the attractiveness and engagement of viewers. This system operates by translating weather data from SQL into key parameters for pre-built 3D visualizations, which appear at marked coordinates. This application runs on the Windows platform and can be managed via Android, allowing presenters to convey engaging scenarios. Research on East Java BMKG data showed that the design function to transform BMKG data into a virtual weather animation data was significantly successful in both synchronizations.
Identifying Financial Literacy and Asset Participation Segments among Young Adults in Indonesia Using K-Means Clustering Oktavia Citra Resmi Rachmawati; Kevin Ilham Apriandy; Kevin Harlis Oktaviano; Zakha Maisat Eka Darmawan
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10684

Abstract

Financial literacy is crucial in influencing asset ownership decisions among young adults; yet, the variability of financial literacy and asset involvement in Indonesia has not been adequately examined. This research seeks to categorize young Indonesian individuals based on financial literacy and asset participation using the K-Means clustering technique. The research employed a quantitative methodology, incorporating exploratory data analysis of a survey dataset comprising 952 participants and 13 variables related to financial literacy, asset involvement, demographic traits, economic education, and financial behavior. Missing values were addressed by group-based mode imputation for categorical variables and mean imputation for numerical variables, followed by encoding and data standardization utilizing StandardScaler. The ideal number of clusters was assessed by the Elbow Method, Silhouette Score, and Davies–Bouldin Index. Despite achieving the highest Silhouette Score at k = 2, the k = 9 model was chosen due to its lower Davies–Bouldin Index and its ability to enable more nuanced responder segmentation. The findings identified nine categories exhibiting varying levels of basic and advanced financial literacy, ranging from very low to very high. These findings offer significant insights for the formulation of targeted financial education initiatives and financial inclusion policies customized to the attributes of various young adult demographics.
Co-Authors Akhmad Alimudin Aliv Faizal Muhammad Amang Sudarsono, Amang Ashafidz Fauzan Dianta Dwi Susanto Fitrah Maharani Humaira Hestiasari Rante Irma Wulandari Kevin Harlis Oktaviano Kevin Ilham Apriandy Kholid Fathoni Laily Asna Safira M Udin Harun Al Rasyid, M Udin Harun Mifta Nauval Harizy Moh Zikky Moh. Zikky Muhammad Agus Zainuddin Oktavia Citra Resmi Rachmawati Rendra Suprobo Aji Robby Alexander Rosiyah Faradisa Tri Budi Santoso Widi Sarinastiti