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All Journal Journal of Information Systems Engineering and Business Intelligence Jurnal Tekno Kompak Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat Indonesian Journal of Data and Science Sewagati Journal of Mathematics and Applied Statistics
Andi Inayah Auliyah
Department Of Information Systems, Institut Teknologi Sepuluh Nopember, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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The Impact of Social Media Engagement on Market Share: A System Dynamics Model Erma Suryani; Rully Agus Hendrawan; Benyamin Limanto; Fatharani Wafda; Inayah Auliyah
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 1 (2022): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.1.71-79

Abstract

Background: Some studies have shown that Return on Total Assets is a strategy to increase market share. Other studies have also shown that social media like WeChat can increase market share. However, no studies have considered Instagram engagement in increasing market share. Objective: This study aims to identify variable linkage that increases market share through a dynamic system approach in small and medium-sized enterprises (SMEs). Methods: Using a System Dynamics approach, this study presents a model simulation with a proposed increase in market share by considering Instagram features. This approach creates a Causal Loop Diagram converted into a simulated Stock Flow Diagram. The value generated from the simulation is validated with the mean comparison and % error variance formulas. Results: Instagram engagement increases market share from 0.009 to 0.018. Such engagement can be increased by posting regularly and doing more activities, such as increasing post frequency, holding contests, and maximizing all features. Conclusion: This study has successfully modeled information technology, i.e., a promotion module on social media. However, this work has not yet demonstrated how the features can gain more market share, so future research is needed.  Keywords: Causal Loop Diagram, Engagement, Market Share, Stock Flow Diagram, System Dynamics
Pengembangan Digitalisasi Industri Jasa Laundry dalam Rangka Meningkatkan Pangsa Pasar pada UKM 3cious Erma Suryani; Rully Agus Hendrawan; Benyamin Limanto; Fatharani Wafda; Inayah Auliyah
Sewagati Vol 6 No 3 (2022)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (272.905 KB) | DOI: 10.12962/j26139960.v6i3.106

Abstract

Perkembangan UMKM di Indonesia dewasa ini semakin marak dan pada akhir tahun 2020 telah mencapai 64.2 juta unit. Perkembangan UMKM ini juga diiringi dengan berjalan nya transformasi digital melalui platform e-commerce, media sosial, dan kanal digital lain nya. Transformasi digital ini dialami oleh berbagai UMKM di Indonesia, tetapi transformasi ini tidak maksimal. Salah satu UMKM tersebut adalah UMKM 3cious. UMKM yang bergerak di jasa laundry sepatu ini beroperasi di Surabaya. UMKM ini telah memanfaatkan transformasi digital ini untuk meningkatkan pangsa pasar mereka, tetapi masih mengalami kendala di dalam penerapan nya. Penerapan transformasi digital memanfaatkan sosial media tidak dilakukan secara maksimal sehingga target pasar yang dituju tidak berkembang. Permasalahan ini perlu ditangani agar tidak mengganggu kelangsungan hidup dari UMKM itu sendiri. UMKM 3cious bersedia untuk menjadi studi kasus di dalam penelitian untuk menemukan serta memperbaiki permasalahan yang dihadapi. Teknik penelitian ini memanfaatkan metode Sistem Dinamik dengan mencari identifikasi dan faktor yang mempengaruhi pangsa pasar dan meningkatkan penjualan yang berakibat meningkatkan profit, serta mengembangkan model serta yang sesuai dengan tujuan dari UMKM 3cious sendiri. Model yang dikembangkan dari penelitian ini dapat digunakan untuk meningkatkan pangsa pasar dari UMKM ini, dan dapat digunakan sebagai rumusan strategi jangka panjang yang berfokus pada peningkatan pangsa pasar dan penjualan.
Implementasi Kombinasi Algoritma Enkripsi Rivest Shamir Adleman (Rsa) dan Algoritma Kompresi Huffman Pada File Document Auliyah, Andi Inayah
Indonesian Journal of Data and Science Vol. 1 No. 1 (2020): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (152.615 KB) | DOI: 10.33096/ijodas.v1i1.6

Abstract

Enkripsi yaitu proses merubah data asli menjadi data samaran dan dekripsi yaitu proses pengembalian data samara menjadi data asli kembali. Terlepas dari masalah keamanan data, masalah lain yang harus kita perhatikan adalah ukuran data atau informasi yang semakin besar oleh karena itu perlu adanya upaya untuk menanggulangi masalah tersebut salah satunya yaitu dengan menggunkan algoritma kombinasi. Maka dari itu penulis mengkombinasikan kedua metode tersebut yaitu metode Enkripsi dan metode Kombinasi untuk melihat manakah cara yang lebih efisien digunakan antara dienkripsi terlebih dahulu kemudian dikompresi atau sebaliknya.Dengan mengkombinasikan kedua algoritma tersebut penulis menemukan bahwa hasil yang diberikan dari kombinasi tersebut tidak maksimal karena ukuran file hasil proses menjadi lebih besar dibandingkan file asli sebelum diproses. Dari hasil analisis data dalam penelitian ini, maka untuk persentasi keberhasilan prosesnya dapat dibagi menjadi empat bagian.yaitu proses enkripsi-kompresi,kompresi-enkripsi,dekompresi-dekripsi dan dekripsi-dekompresi dengan tingkat keberhasilan 100%. Jika dilihat dari proses tingkat keberhasilan sangat memuaskan namun jika dilihat keberhasilan mengembalikan isi file itu dapat dibagi menjadi dua bagian yaitu proses dekompresi-dekripsi persentasi keberhasilannya itu sebanyak 33,34% dan pesentasi kegagalan sekitar 66,67% sedangkan untuk proses dekripsi-dekopresi persentasi keberhasilan sekitar 33,34% dan persentasi kegagalan sekitar 66,67%
Sentiment Analysis of Bapenda South Sulawesi Mobile Application on Google Play Store Using Support Vector Machine Burhan, Muhammad Ikhwan; Ali, Andi Nurfadillah; Auliyah, A. Inayah; Hading, Muhaimin
Journal of Mathematics and Applied Statistics Vol. 2 No. 2 (2024): December 2024
Publisher : Yayasan Insan Literasi Cendekia (INLIC) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/mathstat.v2i2.244

Abstract

This study analyzes user sentiment toward the Bapenda Sulsel Mobile application, an e-government platform developed by the Regional Revenue Agency of South Sulawesi, Indonesia. The research aims to evaluate user feedback and identify areas for improvement to enhance user satisfaction. Using sentiment analysis, user reviews from Google Play Store were collected and classified into positive, negative, and neutral sentiments through the Support Vector Machine (SVM) algorithm. Preprocessing steps such as tokenization, stopword removal, and stemming were applied to prepare the data. Term Frequency-Inverse Document Frequency (TF-IDF) was used for feature extraction to enhance classification accuracy. The SVM model demonstrated an overall accuracy of 80%, achieving a high recall of 98% for positive reviews but only 40% for negative reviews, reflecting challenges in handling class imbalance. Results show that 72% of users expressed positive sentiment, praising the app’s functionality and ease of use. However, 28% of reviews were negative, citing issues like technical bugs and usability challenges The findings highlight the app’s strengths in delivering e-government services and its role in improving tax management. However, the significant proportion of negative feedback emphasizes the need for addressing user concerns. Recommendations include balancing the dataset, refining the SVM model, and prioritizing improvements based on user feedback. This study contributes to the broader understanding of applying sentiment analysis in evaluating e-government platforms and offers actionable insights for enhancing the user experience.
Comparative Analysis of Algorithms for Sensitive Outlier Protection in Privacy Preserving Data Mining Burhan, Muhammad Ikhwan; Ali, Andi Nurfadillah; Auliyah, A. Inayah; Hading, Muhaimin
Jurnal Tekno Kompak Vol 19, No 2 (2025): AGUSTUS (In Progress)
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jtk.v19i2.4754

Abstract

Data mining is a crucial method in the realm of Big Data for extracting valuable predictive insights from extensive datasets. In the contemporary digital landscape, a significant difficulty is preserving individual privacy during data mining, particularly in safeguarding sensitive outliers that may harbour personal information. Outliers are data points that markedly diverge from the overall trend and frequently encompass very specialised or sensitive information. This paper examines the comparative efficacy of various clustering algorithms employed in outlier detection, specifically PAM (Partitioning Around Medoids), CLARA (Clustering Large Applications), CLARANS (Clustering Large Applications Based on Randomised Search), and ECLARANS (Enhanced CLARANS). This study aims to evaluate the efficacy of each algorithm in identifying outliers and to examine the usefulness of the employed privacy protection strategy, specifically the Gaussian Perturbation Random method. This experiment utilises two health datasets: the Diabetes Dataset from the National Institute of Diabetes and Digestive and Kidney Diseases and the Wisconsin Breast Cancer Dataset. The two datasets were chosen because to their multivariate features, which exhibit adequate data variation for outlier detection. The study's results indicate that the CLARA algorithm effectively identified a superior quantity of outliers compared to the other algorithms, with the diabetes dataset exhibiting the greatest count of outliers (65 outliers). The CLARA algorithm shown superiority in identifying outliers within extensive datasets due to the utilisation of a sampling methodology. Conversely, the PAM, CLARANS, and ECLARANS algorithms identified a same quantity of outliers in both datasets. ECLARANS shown superior time efficiency on the diabetic dataset, but CLARA demonstrated the highest efficiency on the breast cancer dataset. The Gaussian Perturbation Random technique was employed for preserving the identified sensitive outliers. The findings indicate that this strategy effectively maintains privacy while ensuring detection accuracy is not compromised. This method provides a dependable means of safeguarding individual privacy in health data mining, a domain characterised by significant privacy concerns.
Implementasi Pembelajaran Berbasis Kecerdasan Buatan Di Upt Sd Negeri 16 Parepare Hading, Muhaimin; Radhiansyah, Radhiansyah; Noor, Nurul Chairunnisa; syahruddin , A. Syahrinaldy; Auliyah, A. Inayah; Ali, Andi Nurfadillah; Burhan, Muhammad Ikhwan; Irsan, Muhammad
Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat Vol. 6 No. 2 (2025): Volume 6, No 2 Juni 2025
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/atjpm.v6i2.6311

Abstract

Pemanfaatan teknologi dalam pendidikan dasar menjadi semakin krusial di tengah perkembangan era digital dan Revolusi Industri 4.0. Kegiatan pengabdian ini dilatarbelakangi oleh pentingnya peningkatan literasi teknologi di lingkungan sekolah dasar, khususnya dalam pemanfaatan kecerdasan buatan (Artificial Intelligence/AI) untuk mendukung proses belajar mengajar yang lebih interaktif dan adaptif. Program ini dilaksanakan di UPT SD Negeri 16 Parepare dengan tujuan utama untuk mengimplementasikan pendekatan pembelajaran berbasis AI. Metode pelaksanaan meliputi pelatihan intensif kepada guru mengenai konsep dan praktik penggunaan AI, penerapan langsung AI dalam kegiatan pembelajaran di kelas, serta evaluasi untuk mengukur dampak kegiatan. Hasil kegiatan menunjukkan adanya peningkatan pemahaman dan keterampilan guru dalam mengintegrasikan AI ke dalam pembelajaran, serta meningkatnya minat dan partisipasi aktif siswa selama proses belajar. Kegiatan ini memberikan kontribusi positif dalam memperkenalkan transformasi digital di lingkungan sekolah dasar, serta berpotensi menjadi model replikasi untuk sekolah lainnya.
Co-Authors Andi Nurfadillah Ali Benyamin Limanto Benyamin Limanto Erma Suryani Fatharani Wafda Fatharani Wafda Hading, Muhaimin Muhammad Ikhwan Burhan Muhammad Irsan Nurul Chairunnisa Noor Radhiansyah, Radhiansyah Rully Agus Hendrawan syahruddin , A. Syahrinaldy