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All Journal Jurnal Pilar Nusa Mandiri Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Techno Nusa Mandiri : Journal of Computing and Information Technology Innovation in Research of Informatics (INNOVATICS) International Journal of Global Operations Research Joutica : Journal of Informatic Unisla Jurnal Pengabdian Masyarakat Tapis Berseri Sainteks: Jurnal Sain dan Teknik
Reni Nursyanti
Universitas Bandar Lampung
Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics

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Pemanfaatan Augmented Reality Untuk Eksplorasi Gunung Berapi Di Jawa Barat Nursyanti, Reni; Budiman; Anto Widianto
Joutica Vol 9 No 2 (2024): SEPTEMBER
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30736/informatika.v9i2.1289

Abstract

Gunung berapi adalah salah satu destinasi yang populer untuk menambah pengalaman baru, dengan banyaknya objek wisata yang dapat dieksplorasi. Gunung Tangkuban Parahu, Gunung Papandayan, dan Gunung Ciremai adalah beberapa gunung di Jawa Barat yang menawarkan beragam pilihan destinasi wisata. Namun, banyak wisatawan yang masih belum mengetahui tempat wisata apa saja yang ada di sekitar gunung-gunung tersebut. Augmented Reality (AR) merupakan teknologi yang dapat digunakan untuk memberikan visualisasi kepada masyarakat, khususnya wisatawan, tentang berbagai tempat wisata dan daerah di sekitar gunung berapi tersebut. Penelitian ini menggunakan metode Luther-Sutopo, yang meliputi pengumpulan data melalui wawancara, observasi, studi literatur, dan studi pustaka. Selain itu, pengujian dilakukan dengan menggunakan kuesioner kepada 20 responden melalui Alpha dan Beta Testing. Hasil pengujian Alpha menunjukkan bahwa penggunaan multimedia untuk eksplorasi gunung berapi di Jawa Barat ini sudah sesuai, dan Beta Testing menunjukkan bahwa 83% responden merasa puas dengan multimedia eksplorasi tersebut. Hasil penelitian ini menunjukkan bahwa multimedia eksplorasi gunung berapi berbasis Augmented Reality di Jawa Barat dapat membantu masyarakat dalam mengeksplorasi gunung berapi di wilayah tersebut.
Digital Marketing bagi Pemula untuk Peningkatan Penjualan Produk UMKM pada Anggota Karang Taruna Budhi Wibawa Nursyanti, Reni; M, Marwodo
Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) Vol. 2 No. 2 (2023): Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) (Edisi Oktober)
Publisher : Pusat Studi Teknologi Informasi Fakultas Ilmu Komputer Universitas Bandar Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jpmtb.v2i2.69

Abstract

Pada era digital seperti saat ini banyak generasi muda yang memulai langkah menjadi wirausaha yang membutuhkan media pemasaran menggunakan media digital tersebut. Untuk mengatasi permasalahan tersebut maka dibutuhkan  pelatihan dan pendampingan yang dapat memberikan informasi sekaligus pelatihan  dengan tema Penggunaan Media Sosial Untuk Meningkatkan Penjualan Produk UMKM Anggota Karang Taruna Budhi Wibawa di Kelurahan Kopo. Tujuannya adalah memberikan pemahaman tentang pentingnya pembuatan konten produk di platform media sosial bagi pelaku UMKM anggota Karang Taruna dalam meningkatkan pendapatan. Target luaran kegiatan yaitu memberikan pengetahuan pentingnya pembuatan konten produk bagi para pelaku UMKM anggota Karang Taruna, menambah softskill tentang wawasan bagaimana mengelola produk dan pemasaran berbasis informasi teknologi, dan menumbuhkan jiwa technopreneur pada para pelaku UMKM anggota Karang Taruna.
Optimasi Penggunaan Teknologi Dan Akses Digital Untuk Pendidikan Lanjutan Pada Kober Nurul Ikhlas Nursyanti, Reni; Setiana, Elia; Marwondo; Restreva Danestiara, Venia; Prakarsa, Graha; Ikhsan Nur, Muhammad; Teofilus Hendrawan, Yesaya
Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) Vol. 3 No. 2 (2024): Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) (Edisi Oktober)
Publisher : Pusat Studi Teknologi Informasi Fakultas Ilmu Komputer Universitas Bandar Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jpmtb.v3i2.114

Abstract

Pemerintah, khususnya Dinas Pendidikan, semakin mendorong pemanfaatan teknologi dalam mendukung pendidikan lanjutan. Salah satu langkah konkret yang diambil adalah implementasi sistem Penerimaan Peserta Didik Baru (PPDB). Inisiatif ini menjadi bagian dari upaya untuk meningkatkan efisiensi dan aksesibilitas dalam proses pendidikan. Melalui PPDB online, calon siswa dan orang tua dapat mengakses informasi dan melakukan pendaftaran tanpa harus datang ke lokasi secara fisik. Hal ini memungkinkan partisipasi yang lebih luas dan meminimalkan hambatan administratif. Penerapan teknologi dalam PPDB online juga membawa dampak positif dalam hal transparansi. Meskipun Prosedur pendaftaran dan kriteria seleksi menjadi lebih jelas dan terdokumentasi dengan baik, tetapi dalam prosesnya orang tua siswa masih banyak yang belum mengerti penggunaan teknologi dan alur sistem PPDB Onlie serta apa saya yang perlu dipersiapkan saak mengakses teknologi tersebut, sehingga PKM ini diadakan agar dapat pengoptimalisasi penggunaan teknologi sekaligus mengedukasi orang tua siswa untuk dapat lebih efektif dalam menggunakan teknologi terutama akses digital untuk Pendidikan lanjutan.
Development of UTAUT Model with Hedonic Motivation to Measure the Adoption of E-Marketplace Mobile Application in Indonesia Prakarsa, Graha; Nursyanti, Reni
Sainteks: Jurnal Sain dan Teknik Vol 7 No 01 (2025): Maret
Publisher : Universitas Insan Cendekia Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37577/sainteks.v7i01.874

Abstract

The development of internet users in Indonesia makes people enthusiastic about using internet services in conducting online shopping transactions using mobile e-marketplaces. This research is motivated by the main problem, namely the absence of measurements in previous studies that measure acceptance of the use of mobile e-marketplaces using the UTAUT model so that application developers can find out what factors can influence users in adopting existing mobile e-marketplaces. market system. in Indonesia. The data processed are 100 respondents who have experience using e-marketplaces on a mobile basis, then analyzed using PLS (Partial Least Squares). This study uses the conceptual framework of the Unified Theory of Acceptance and Use Technology (UTAUT) model. The constructs used in this study are Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Condition, in addition to the Hedonic Motivation variable. The results of the analysis show that Effort Expectancy and Hedonic Motivation have a significant effect on the actual use of E-Marketplace Mobile users in Indonesia, while Performance Expectancy, Social Influence and Facilitating Conditions have no significant effect on the actual use of E-Marketplace Mobile users in Indonesia. Keywords: : UTAUT, Hedonic Motivation, E-marketplace
OPTIMIZED FACEBOOK PROPHET FOR MPOX FORECASTING: ENHANCING PREDICTIVE ACCURACY WITH HYPERPARAMETER TUNING Alamsyah, Nur; Restreva Danestiara, Venia; Budiman, Budiman; Nursyanti, Reni; Setiana, Elia; Hendra, Acep
Jurnal Techno Nusa Mandiri Vol. 22 No. 1 (2025): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v22i1.6507

Abstract

MPOX (Monkeypox) has become a significant global health concern, requiring accurate forecasting for effective outbreak management. This study improves MPOX case prediction using Facebook Prophet with hyperparameter optimization. The dataset consists of global MPOX case records collected over time. Data preprocessing includes missing value imputation, normalization, and aggregation. Facebook Prophet is applied to forecast case trends, with model performance evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). A baseline Prophet model is first trained using default parameters. The model is then optimized by fine-tuning seasonality mode, changepoint prior scale, and growth model. The results show that hyperparameter tuning significantly enhances forecasting accuracy. The optimized model reduces MSE from 541,844.77 to 320,953.34 and RMSE from 736.10 to 566.53, demonstrating improved precision. The model also captures trend shifts and seasonal fluctuations more effectively. In conclusion, this study confirms that tuning Facebook Prophet improves epidemic forecasting, making it a reliable tool for MPOX monitoring. Future research should integrate external factors, such as vaccination rates and mobility data, to further refine predictions.
Pelatihan Pengenalan Cara Kerja Search Engine Marketing untuk Platfom Digital Marketing kepada siswa SMAN 16 Bandung Akbar, Imannudin; Nursyanti, Reni; Ramadhani, Muhammad Wahyu; Setiawan, Dani
Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) Vol. 4 No. 1 (2025): Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) (Edisi April)
Publisher : Pusat Studi Teknologi Informasi Fakultas Ilmu Komputer Universitas Bandar Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jpmtb.v4i1.137

Abstract

Over time, companies have begun transitioning their marketing systems from conventional methods to modern approaches by leveraging the internet. This shift is further supported by the ease of accessing social media, conducting transactions, and communicating online, all of which make internet-based marketing increasingly advantageous. The massive number of internet users and the high engagement with digital media have given rise to new business potentials and opportunities, widely known as digital marketing.Digital marketing strategies are estimated to influence up to 78% of a business unit’s competitive advantage in promoting its products. It enables adaptive digital relevance across a series of marketing activities, institutions, processes, and customers. This, in turn, drives a 20% annual growth in customers transitioning to digital platforms, with younger users becoming a dominant group of consumers.Search Engine Marketing (SEM) is an online marketing strategy designed to increase website visibility on search engine results pages. SEM serves as an effective promotional tool and is one of the fastest ways to direct potential consumers to a website, as effective SEM can position a site on the first page of search engine results.
Approximate Bayesian Inference for Bayesian Confidence Quantification in DNA Sequence Classification Using Monte Carlo Dropout Approach Alamsyah, Nur; Budiman, Budiman; Nursyanti, Reni; Setiana, Elia; Danestiara, Venia Restreva
Innovation in Research of Informatics (Innovatics) Vol 7, No 1 (2025): March 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i1.14349

Abstract

Splice junction classification in DNA sequences is critical for understanding genetic structures and processes, particularly the differentiation between exon-intron (EI), intron-exon (IE), and neither boundaries. Traditional neural network models achieve high accuracy but often lack the ability to quantify uncertainty, which is essential for reliability in sensitive applications such as bioinformatics. This study addresses this limitation by incorporating Bayesian confidence quantification into DNA sequence classification using the Monte Carlo Dropout (MCD) approach. A baseline neural network was first implemented as a reference, achieving a test accuracy of 95.61%. Subsequently, MCD was applied, which not only improved the test accuracy to 96.03% but also provided uncertainty estimation for each prediction by sampling multiple inferences. The uncertainty values enabled the identification of low-confidence predictions, enhancing the interpretability and reliability of the model. Experiments were conducted on a binary-encoded DNA sequence dataset, representing nucleotides (A, C, G, T) and their splice junctions. The results demonstrated that MCD is a robust approach for DNA sequence classification, offering both high predictive performance and actionable insights through uncertainty quantification. This research highlights the potential of Bayesian confidence quantification in genomic studies, particularly for tasks requiring high reliability and interpretability. The proposed approach bridges the gap between accurate predictions and the need for robust uncertainty estimation, contributing to advancements in bioinformatics and machine learning applications in genetic research.
Information Quality and Compatibility as Determinants of M-Wallet Usage in Indonesia. Prakarsa, Graha; Nursyanti, Reni; Putra, Prayuda Mulyadi; Saputra, Renda Sandi
International Journal of Global Operations Research Vol. 6 No. 3 (2025): International Journal of Global Operations Research (IJGOR), August 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i3.393

Abstract

This study aims to assess the acceptance of mobile wallet applications in Indonesia by incorporating Information Quality and Compatibility as external factors within the framework of the Technology Acceptance Model (TAM). A quantitative approach was employed, and data from 208 respondents were analyzed using Partial Least Squares - Structural Equation Modeling (PLS-SEM). The findings indicate that both Information Quality and Compatibility have a positive and significant influence on Perceived Usefulness and Perceived Ease of Use. Furthermore, these two variables also significantly affect Continuance Intention to Use, which subsequently impacts the Actual Use of mobile wallets. Overall, Information Quality and Compatibility contribute 56% to Perceived Usefulness, 52.4% to Perceived Ease of Use, and 43.8% to Continuance Intention to Use. These findings offer valuable insights for application developers seeking to enhance mobile wallet adoption in Indonesia.
ISOLATION FOREST PARAMETER TUNING FOR MOBILE APP ANOMALY DETECTION BASED ON PERMISSION REQUESTS Kaunang, Valencia Claudia Jennifer; Alamsyah, Nur; Nursyanti, Reni; Budiman, Budiman; Danestiara, Venia R; Setiana, Elia
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.6647

Abstract

Ensuring mobile app security needs the capability to detect apps that request excessive or inappropriate permissions. This research proposes an anomaly detection approach using Isolation Forest, enhanced through hyperparameter tuning, to identify suspect apps based on permission request patterns. The dataset is processed into binary features, followed by exploratory data analysis (EDA) to examine the distribution and highlight sensitive permissions. The Isolation Forest model is then optimized by tuning parameters such as contamination level, number of estimators, and sample size. The fine-tuned model achieved a more accurate separation between normal and anomaly applications, detecting 10 anomalies out of 200 applications, with anomaly applications averaging 125.10 permits compared to 42.76 in normal applications. These anomalies often requested permissions related to network, storage, contacts and microphone, indicating potential privacy risks. The results show that parameter tuning improves the detection performance of Isolation Forest, providing a practical solution for mobile security monitoring. After tuning, the number of false positives decreased by 50%, and the model successfully reduced detected anomalies from 20 to 10, increasing the precision of anomaly detection from 70% to 90%. Future work could include improving feature selection and integration into real-time detection systems. 
Optimation of Social Assistance Recipient Determination Using K-Means Clustering Algorithm and K-Nearest Neighbour Algorithm Prakarsa, Graha; Rahadiyanti, Nira; Nursyanti, Reni; Hadiantini, Ratih
Sainteks: Jurnal Sain dan Teknik Vol 7 No 02 (2025): September
Publisher : Universitas Insan Cendekia Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37577/sainteks.v7i02.952

Abstract

Determining the status of the family as recipients of assistance is very important, so that aid can be distributed accurately. Data mining takes advantage of experience or even mistakes in the past to add quality based on examples as well as the results of the analysis, one of which uses the capabilities of data mining techniques, namely clustering & classification. The purpose of this research is to determine the right beneficiaries. K-Means Clustering and K-Nearest Neighbor are 2 data mining problem solving algorithms used in selecting beneficiaries. Both of these troubleshooting algorithms make good performance. However, to be widely used, it is necessary to research which algorithm has higher accuracy. Based on this, in this study a comparison of the K-Means Clustering and K-Nearest Neighbor algorithms was carried out on the problem of selecting beneficiaries. Comparisons were made using 1760 data. Based on the tests that have been carried out, beneficiaries using k-means clustering got as much as 65.145% while K-Nearest Neighbor as much as 99.6501%. This shows that the K-Nearest Neighbor problem solving algorithm has higher accuracy.
Co-Authors Acep Hendra Akbar, Imannudin Anto Widianto Budiman Budiman Danestiara, Venia R Dani Setiawan Graha Prakarsa Ikhsan Nur, Muhammad Kaunang, Valencia Kaunang, Valencia Claudia Jennifer M, Marwodo Marwondo Nur Alamsyah Putra, Prayuda Mulyadi Rahadiyanti, Nira Ramadhani, Muhammad Wahyu Saputra, Renda Sandi Setiana, Elia Teofilus Hendrawan, Yesaya Titan Parama Yoga Venia Restreva Danestiara