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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi Compiler Applied Science and Technology Research Journal Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen
Dwijayanti, Irmma
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Klasterisasi Segmentasi Pola Penyewaan Lapangan Mini Soccer di Yogyakarta Menggunakan Algoritma K-Means Anggraini, Deviana Dyah; Aksan, Azzikra Ramadhanti; Dwijayanti, Irmma; Maulana Ridwan, Muhamad Fikry
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 23 No 1 (2025): Januari 2025
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v23i1.151

Abstract

Sepak bola mini (mini soccer) adalah cabang olahraga sepak bola yang dimainkan di lapangan berukuran lebih kecil dengan jumlah pemain lebih sedikit, sehingga menjadi pilihan populer terutama di perkotaan dengan keterbatasan lapangan besar. Namun, pengelola sering menghadapi kesulitan dalam meratakan waktu penyewaan, karena jam tertentu cenderung menjadi favorit, sementara jam lainnya kurang diminati. Penelitian ini bertujuan untuk menganalisis pola penyewaan lapangan mini soccer berdasarkan waktu penyewaan guna mendukung manajemen dalam menentukan strategi bisnis yang efektif. Penelitian ini menggunakan metode klastering dengan algoritma K-Means. Analisis dilakukan untuk mengelompokkan data penyewaan berdasarkan parameter seperti waktu mulai, durasi, dan frekuensi transaksi. Hasilnya menunjukkan data penyewaan dapat dikelompokkan ke dalam beberapa cluster, yaitu cluster tinggi dan rendah yang mencerminkan segmentasi pola penyewaan lapangan. Setiap cluster memberikan wawasan tentang perilaku penyewa, seperti waktu penyewaan paling populer, durasi rata-rata, dan preferensi hari tertentu. Informasi ini membantu manajemen merancang strategi terarah, seperti promosi atau bundling paket. Penelitian membuktikan algoritma K-Means efektif dalam memahami pola penyewaan dan mendukung pengambilan keputusan berbasis data. Implikasi hasil ini dapat meningkatkan pengelolaan operasional serta loyalitas pelanggan melalui strategi bisnis yang lebih relevan dan kompetitif.
Analisis Retensi Pengguna Mobile JKN dengan HEART Metrics dan Regresi Linier Berganda Panca, Agung Satria; Sobri, Mohammad; Dwijayanti, Irmma; Hidayatullah, Syarief
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 23 No 2 (2025): Mei 2025
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v23i2.176

Abstract

Digitalisasi layanan kesehatan mendorong BPJS Kesehatan mengembangkan platform seperti Mobile JKN sebagai layanan kesehatan digital. Meskipun aplikasi ini telah diluncurkan secara luas, Minimnya pemanfaatan aplikasi secara berulang menunjukkan tantangan dalam mempertahankan pengguna aktif. Oleh karena itu, penting untuk mengevaluasi pengalaman pengguna guna memastikan aplikasi tidak hanya diunduh, tetapi juga digunakan secara berkelanjutan. Penelitian ini bertujuan untuk menganalisis faktor-faktor yang memengaruhi retensi pengguna terhadap aplikasi Mobile JKN dengan pendekatan HEART Metrics, yang mencakup lima dimensi utama: Happiness, Engagement, Adoption, Retention, dan Task Success. Data dikumpulkan melalui penyebaran kuesioner kepada 107 pengguna aktif, kemudian dianalisis menggunakan regresi linier berganda. Hasil analisis menunjukkan bahwa dimensi Engagement dan Task Success memiliki pengaruh signifikan terhadap retensi pengguna, sementara dimensi lainnya tidak menunjukkan pengaruh yang signifikan secara statistik. Temuan ini mengindikasikan bahwa keterlibatan pengguna dan kemudahan dalam menyelesaikan tugas menjadi faktor penting dalam mempertahankan penggunaan aplikasi. Berdasarkan hasil tersebut, disarankan agar pengembangan aplikasi Mobile JKN difokuskan pada peningkatan interaktivitas serta penyederhanaan antarmuka guna meningkatkan kenyamanan dan loyalitas pengguna.
Tweets Classification of Mental Health Disorder in Indonesia Using LDA and Cosine Similarity Dwijayanti, Irmma; Habibi, Muhammad; Kusumaningtyas, Kartikadyota; Riyadi, Sujono
Telematika Vol 21 No 1 (2024): Edisi Pertama 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i1.10725

Abstract

Purpose: Twitter related to mental health has great potential as a medium to provide important information to the public and health organizations on a large scale, but an evaluation of tweet data related to mental health disorders has not been carried out. This study aims to classify tweet data to determine the most common mental health disorders in Indonesia based on the symptoms experienced.Methodology: The classification process is carried out using cosine similarity calculations between tweets data and keywords which are compiled based on theoretical studies and optimization of the LDA topic modeling results.Findings/result:The classification results show that the most discussed issues on Twitter are depression, bipolar, schizophrenia, dementia, and PTSD. Based on these results it can be interpreted that the level of prevalence and public attention to depressive diorders is quite high compared to other disorders. From the results of the classification, it is also possible to identify the most discussed symptoms throughthe emergence of keywords from each category.Originality: Classification is calculated based on the cosine similarity between tweets and keywords compiled from human judgement and enriched using the results of LDA topic modeling to improve classification performance
Tweet Analysis of Mental Illness Using K-Means Clustering and Support Vector Machine Kusumaningtyas, Kartikadyota; Habibi, Muhammad; Dwijayanti, Irmma; Sumiyarini, Retno
Telematika Vol 20 No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.9820

Abstract

Purpose: Social media, particularly Twitter, provides a venue for individuals to share their thoughts. The public's perception of mental illnesses is often debated on Twitter. So yet, no evaluation of community tweets connected to data on mental health conditions has been performed. The purpose of this study is to examine tweets linked to mental illnesses in Indonesia in order to identify the themes of conversation and the polarity trends of these tweets.Design/methodology/approach: To address this issue, the K-Means Clustering algorithm is utilized to aggregate tweet data that is used to find themes of conversation. The emotion polarity value of each cluster result was then determined using the Support Vector Machine (SVM) approach.Findings/results: This study generated five topic clusters based on tweets about mental illness. While sentiment analysis revealed that all clusters had more negative sentiment classes than positive. Cluster 4 and Cluster 5 had the highest number of negative sentiment values. These clusters emphasize the necessity of consulting with psychiatrists and psychologists if people have mental health disorders, as well as financing for mental health disorder treatment through BPJS Kesehatan services.Originality/value/state of the art: The analysis was done in two stages: data grouping to find themes of conversation using K-Means clustering and SVM to look for positive and negative polarity values associated to twitter data about mental illness.
Analysis Experience New Users of Flo App Based on Group Age with the User Experience Questionnaire (UEQ) Maulana Ridwan, Muhamad Fikry; Purwenti, Devita Ayu; Amsori, Trenggar S D C; Dwijayanti, Irmma
APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL Vol. 4 No. 2 (2025): Applied Science and Technology Research Journal
Publisher : Lembaga Penelitian dan Pengabdian Mayarakat (LPPM) Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/astro.v4i2.8013

Abstract

The advancement of digital technology has significantly driven innovation in health applications, offering users practical tools to monitor their physical and emotional well-being. Among these, Flo: Period & Pregnancy Tracker stands out as a popular application designed to help women track their reproductive cycles, ovulation, and associated hormonal symptoms. This study aims to evaluate the user experience of new users of the Flo application across two age groups: 12–25 years and 26–45 years, to understand their perceptions of comfort and ease of use, employing a quantitative approach with the User Experience Questionnaire (UEQ). Analysis results indicate that both age groups generally provided positive assessments of the application. The Stimulation and Efficiency aspects received the highest scores, while Novelty was the lowest-scoring aspect. Further analysis revealed that the 12– 25 year age group tended to prioritize hedonic qualities (such as Stimulation and Attractiveness), whereas the 26– 45 year age group valued pragmatic qualities (such as Efficiency and Perspicuity) more in their initial app usage experience. These findings underscore the importance of UI/UX design that adapts to the differing needs and expectations of users across age segments for overall experience improvement. It is important to note that the imbalance in the number of respondents between age groups is a limitation of this study, which may affect the validity of peer-to-peer comparisons and the generalizability of results due to constraints in time and primary respondent data availability.
Topic Analysis of Indonesian Online News on the Free Nutritious Meal Program Using Non-Negative Matrix Factorization Dwijayanti, Irmma; Lahitani, Alfirna Rizqi; Habibi, Muhammad
Compiler Vol 14, No 2 (2025): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v14i2.3499

Abstract

The Free Nutritious Meal Program (MBG) represents a key policy of the Indonesian government to address malnutrition and stunting by providing nutritious meals for students. This study applies Non-Negative Matrix Factorization (NMF) for topic modeling on a long-text corpus of 5,390 digital news articles collected from seven national portals, with the aim of mapping public discourse on MBG. The optimal number of topics was determined using the coherence score, yielding nine distinct themes. Findings indicate that media coverage primarily revolves around program distribution in schools, the role of Micro, Small, and Medium Enterprises (MSMEs) and the food sector, budget allocation, political dynamics of national figures, and health-related concerns such as student poisoning cases. The results suggest that MBG is widely perceived as a strategic policy with broad implications for public policy, economic development, political debate, and social welfare. Methodologically, this research demonstrates the effectiveness of NMF in identifying latent thematic structures within long-text news corpora, offering insights into how digital media frames and interprets government initiatives.
Co-Authors Aksan, Azzikra Ramadhanti Amsori, Trenggar S D C Anggraini, Deviana Dyah Hidayatullah, Syarief Kusumaningtyas, Kartikadyota Lahitani, Alfirna Rizqi Maulana Ridwan, Muhamad Fikry Muhammad Habibi Panca, Agung Satria Purwenti, Devita Ayu Sobri, Mohammad Sujono Riyadi Sumiyarini, Retno