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All Journal Elkom: Jurnal Elektronika dan Komputer Sinergi Format : Jurnal Imiah Teknik Informatika Jurnal Ilmiah FIFO Faktor Exacta Jurnal SOLMA AMMA : Jurnal Pengabdian Masyarakat
Indriyanti, Prastika
Universitas Mercu Buana
Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing Other

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Penerapan Decision tree dalam pengambilan keputusan untuk pemain Texas Holdem Poker Prastika Indriyanti; Muhamad Fazalika Hismawan; Mujiono Mujiono
Jurnal Ilmiah FIFO Vol 12, No 2 (2020)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441//fifo.2020.v12i2.006

Abstract

Abstract Texas holdem poker is a popular poker game. This game is played by millions of people every day, both to find additional income or just for fun. But in fact not everyone who plays poker with these goals gets according to what they have planned. Some people actually experience losses after playing. They unconsciously develop the logic of gambler's fallacy that causes them to play poker without using strategy.  This research made a system that can prove the defect of gambler's fallacy and made a tool for playing poker. The processed dataset is a dataset containing information of cards used in poker and their probability of occurrence. The method used in this research are iterative deepening search tree and decision tree. The main results of this research is a tool that can provide insight as a basis for decision making. However, this tool has not been able to prove its capabilities in helping to increase the winning percentage, so that further study is needed. In addition, this study also shows that playing with gambler's fallacy logic only gives 48.13% wins of 6,000 trials. These results proved that using gambler's fallacy logic in playing poker is a mistake.Keywords – Tree, Poker, Probability, Gambler’s Fallacy Abstrak Texas holdem poker merupakan permainan poker yang populer. Permainan ini dimainkan oleh jutaan orang setiap harinya, baik untuk mencari penghasilan tambahan ataupun hanya untuk bersenang-senang. Namun pada kenyataanya tidak semua orang yang bermain poker dengan tujuan tersebut mendapatkan sesuai dengan apa yang mereka rencanakan. Sebagian orang justru mengalami kerugian pasca bermain. Mereka tanpa sadar mengembangkan logika berpikir gambler’s fallacy yang mengakibatkan bermain tanpa strategi. Makalah ini menyajikan hasil studi penerapan pohon keputusan dan pohon pencarian untuk membuktikan kecacatan gambler’s fallacy dan membantu dalam bermain poker. Dataset yang diolah adalah dataset yang berisi informasi kartu-kartu yang dipakai dalam permainan serta probabilitas kemunculannya. Metode yang digunakan pada penelitian ini adalah metode pencarian pada struktur tree dan metode pohon keputusan. Hasil utama dari penelitian ini adalah alat bantu yang mampu memberikan insight sebagai dasar pengambilan keputusan.  Namun, alat bantu ini belum bisa dibuktikan kapabilitasnya dalam membantu menaikkan persentase kemenangan sehingga diperlukan studi lanjutan. Selain itu, penelitian ini juga menunjukkan bahwa bermain dengan logika gambler’s fallacy hanya memberikan 48.13% kemenangan dari 6000 percobaan. Hasil tersebut membuktikan bahwa menggunakan logika gambler’s fallacy dalam bermain poker merupakan suatu kesalahan.Kata kunci – Tree, Poker, Probabilitas, Gambler’s Fallacy 
Pelatihan Pemanfaatan Digital Marketing dan Manajemen Kewirausahaan bagi Pelaku UMKM dengan menggunakan Aplikasi Google My Business Arafat Febriandirza; Faldy Irwiensyah; Firman Noor Hasan; Prastika Indriyanti
Jurnal SOLMA Vol. 10 No. 1s (2021): Spesial Issue
Publisher : Universitas Muhammadiyah Prof. DR. Hamka (UHAMKA Press)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/solma.v10i1s.6514

Abstract

Kegiatan pengabdian ini dilatarbelakangi permasalahan mitra yang meliputi : (a) Kurangnya ilmu digital marketing yang dimiliki oleh pelaku UMKM sebagai sarana untuk menarik minat konsumen dengan teknologi informasi; (b) Perlu peningkatan pengetahuan pelaku UMKM dalam manajemen kewirausahaan untuk meningkatkan penjualan produk dalam skala yang lebih luas. Kegiatan ini bertujuan untuk membantu masalah yang dihadapi UMKM Tahu Sumedang Gurih. Adapun solusi yang ditawarkan ialah memberi pelatihan pemanfaatan digital marketing dan manajemen kewirausahaan dengan menggunakan aplikasi Google My Business. Mitra dalam kegiatan pengabdian ini adalah UMKM Tahu Sumedang Gurih, dan peserta yang mengikuti pelatihan sebanyak tujuh orang. Adapun metode yang digunakan dalam kegiatan ini adalah terdiri dari Identifikasi Masalah Bersama Mitra, Perumusan Permasalahan Mitra Bersama Mitra, Perumusan Solusi Permasalahan Mitra Bersama Mitra, Penyusunan Materi dan Instrumen, Pelaksanaan Kegiatan Pelatihan dan Sosialisasi, Pendampingan, Evaluasi Program Bersama Mitra, serta Penyusunan Laporan dan Luaran. Hasil dari pelaksanaan kegiatan pengabdian ini yaitu memberikan pemahaman tentang pemanfaatan digital marketing dan manajemen kewirausahaan dengan menggunakan Google My Business. Dengan adanya kegiatan pengabdian ini, mitra akan menjadi terbiasa menggunakan aplikasi Google My Business, sehingga ketika mitra menjalankan digital marketing dapat membantu meningkatkan penjualan produk dalam skala lebih luas.
The use of Fuzzy Logic Controller and Artificial Bee Colony for optimizing adaptive SVSF in robot localization algorithm Suwoyo, Heru; Hajar, Muhammad Hafizd Ibnu; Indriyanti, Prastika; Febriandirza, Arafat
SINERGI Vol 28, No 2 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2024.2.003

Abstract

The objective of solving feature-based localization problems is to estimate the path of the robot referring to a given map. Thus, it is not surprising that robust estimators such as Smooth Variable Structure Filter (SVSF) are often used to handle this problem. Basically, its use is highly dependent on an accurate system model and known statistical noise. Where neither of these are available by definition. Therefore, the conventional way is not recommended and the use of an adaptive filter approach can be involved. Based on this and although only partially, Innovation Adaptive Estimation (IAE) has been considered to have a positive influence on improving the performance of the estimator. But not infrequently the solutions offered by this approach also lead to divergences due to unmapped dynamic conditions. Moreover, in this proposal, IAE is enhanced by applying Artificial Bee Colony-Tuned Fuzzy Logic. The hope is that there is quality control for the process noise covariance Q and R measurements by updating them based on the output of this ABC-Tuned FLC.
Pemanfaatan Chi Square dan Ensemble Tree Classifier pada Model SVM, KNN dan C4.5 dalam Penjualan Online Indriyanti, Prastika; Gunawan, Wawan
Faktor Exacta Vol 17, No 3 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i3.24149

Abstract

This research aims to assist MSMEs in overcoming problems in online sales. Currently, sellers only prepare stock without knowing how well the products are sold in their market segment. In the city of Tangerang alone, there are 222,602 MSMEs with various product categories. Therefore, besides utilizing offline sales, business actors should also engage in online sales. This research conducts feature selection using the Chi-Square method and Ensemble Tree Classifier to select the top 6 and 10 features. The SVM, KNN, and C4.5 algorithms are used to build prediction models based on the selected features. Using feature selection, it was found that the influential features are Estimated Shipping Cost, Shipping Cost Paid by Buyer, Total Product Price, and Estimated Shipping Cost Discount. The evaluation results using the three algorithms, SVM, KNN, and C4.5, indicate that the highest accuracy value is obtained when using the C4.5 model with data from the ensemble tree classifier with 6 features at 0.86%, followed by the C4.5 model with 10 features, KNN with 6 features, and KNN with 10 features, all of which source data from the ensemble tree classifier with an accuracy value of 0.85%.
Pelatihan Pembuatan Daftar Pustaka Menggunakan Aplikasi Mendeley Febriandirza, Arafat; Indriyanti, Prastika
AMMA : Jurnal Pengabdian Masyarakat Vol. 4 No. 6 : Juli (2025): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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

Abstract

Many students struggle with manually creating bibliographies for their academic papers, a crucial step to prevent plagiarism. The Mendeley application offers a solution by automatically generating reference lists, yet not all students are familiar with it. Therefore, an online training session was held via Zoom for the general student population. The goal was to equip them with practical knowledge and skills in using Mendeley, enabling them to manage references independently for various academic writings such as journals and theses. This training is expected to not only enhance individual competencies but also contribute to improving the quality of education in Indonesia.
Application of RED and PCQ Algorithms for Network Traffic Management in CBT Systems Prastika Indriyanti; Abdurohman; Rahman Hakim; Adi Affandi Rotib; Silviana Windasari
Elkom: Jurnal Elektronika dan Komputer Vol. 18 No. 1 (2025): Juli : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/tcjwkj28

Abstract

The digital transformation in education has encouraged the adoption of computer-based tests (CBT) using video content, which demands stable and efficient network performance. This study aims to evaluate the performance of two queue management algorithms, namely Random Early Detection (RED) and Per Connection Queue (PCQ), in maintaining the quality of service (QoS) of school networks during online video-based examinations. A case study approach was applied using a real network topology in a school environment, and QoS parameters such as throughput, delay, packet loss, and jitter were measured. The implementation was conducted using a MikroTik RB450Gx4 router configured with simple queue settings for each algorithm. The results show that PCQ provides more consistent performance under high user loads, achieving an average throughput of 56,482 bps and lower delay compared to RED. Conversely, RED performs better in scenarios with a small number of users. The study recommends using PCQ for networks with dynamic and dense user environments, while RED is more suitable for low-traffic conditions where latency stability is crucial. These findings offer practical guidance for managing bandwidth and improving the quality of CBT delivery in educational settings.
Kinerja Komparatif LSTM dan XGBoost untuk Peramalan Radiasi Matahari Perkotaan Tropis Indriyanti, Prastika; Fajriah, Riri
FORMAT Vol 14, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2025.v14.i2.010

Abstract

The increasing reliance on clean energy has accelerated the development of solar energy infrastructure. However, its intermittent nature—especially in tropical urban climates—poses significant challenges to maintaining grid stability. This study compares the performance of two machine learning algorithms, Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost), for hourly solar irradiance forecasting in two climatically distinct tropical cities: Jakarta and Bogor. Using a 10-year historical dataset from NASA POWER that includes solar irradiance and relevant meteorological variables, this research addresses the gap in comparative analysis of deep learning versus ensemble models within high-granularity tropical data settings. The methodology involves data acquisition, preprocessing, feature engineering, model development, hyperparameter tuning, and evaluation using RMSE, MAE, and R² metrics. The results show that LSTM consistently outperforms XGBoost in both cities. In East Jakarta, LSTM achieved a RMSE of 29.24, MAE of 15.63, and R² of 0.9875, compared to XGBoost with RMSE of 38.65, MAE of 18.92, and R² of 0.9782. Similarly, in Bogor Regency, LSTM achieved RMSE of 30.73, MAE of 16.89, and R² of 0.9862, outperforming XGBoost which recorded RMSE of 38.41, MAE of 18.68, and R² of 0.9785. These findings highlight LSTM's superior ability to capture complex temporal dependencies and nonlinear trends in solar irradiance time-series data, especially under the fluctuating weather patterns characteristic of tropical urban environments. The results provide strong empirical support for implementing LSTM-based forecasting in solar energy management systems across similar geographic regions.
Co-Authors Abdurohman Adi Affandi Rotib Fajriah, Riri Faldy Irwiensyah Febriandirza, Arafat Firman Noor Hasan Muhamad Fazalika Hismawan Muhammad Hafizd Ibnu Hajar Mujiono Mujiono Rahman Hakim Silviana Windasari Suwoyo, Heru Wawan Gunawan