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All Journal Applied Information System and Management Jurnal Informasi dan Teknologi Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Sukma Evadini
Unknown Affiliation
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mechanical Engineering

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Analisis Faktor Risiko Kematian dengan Penyakit Komorbid COVID-19 menggunakan Algoritma ECLAT Sukma Evadini
Jurnal Informasi dan Teknologi 2022, Vol. 4, No. 1
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v4i1.181

Abstract

The death rate due to infection with the COVID-19 virus is increasing. Throughout 2020, COVID-19 cases continued to increase with a total of 2,995,758 positive cases with a total death toll of 204,987 in more than 213 infected countries. The increasing number of deaths is certainly a problem that needs special attention. One of the factors that can affect the severity of COVID-19 infection is a medical condition. These medical conditions are referred to as comorbid or comorbid conditions. This study aims to analyze the risk factors for death of COVID-19 patients based on comorbid diseases using the Data Mining technique. The algorithm used is ECLAT. The results of this study are age and comorbid diseases have an influence on the patient's condition when discharged from the hospital with a support value of 25% and a confidence value of 100%.
Analisa Faktor yang Mempengaruhi Kondisi Kesehatan Menggunakan Algoritma Frequent Pattern Growth Sukma Evadini; Alwis Nazir; Yusra Pizaini
Applied Information System and Management (AISM) Vol 1, No 1 (2018): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1113.969 KB) | DOI: 10.15408/aism.v1i1.8646

Abstract

Health is an important factor in human life that have to be guarded, both physically and mentally. This study aimed to analyze the factors that affect health condition using medical check up data. Factors analyzed were consuming alcohol, smoking, exercise, age and gender. The method was the association rule using FPGrowth. The result of this study was factors that affect the health condition is alcohol, exercise and age. This result evidenced by the rules A3→K3, which means that if a person consumes more alcohol than 4 days/week with the amount of alcohol is less than 180ml/day, then health condition was poor with 11% support and 67% confidence. E1→K3, which means that if one rarely exercise then health condition was poor with 24% support and 99% confidence. G2→K3, which means that if a person in middle age group, then the condition of health was poor with 24% support and 99% confidence.
Comparative Analysis of Deep Learning Models for Predicting Undernourishment Prevalence in Indonesia Sukma Evadini; Nadya Satya Handayani
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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

Abstract

Undernourishment constitutes a critical public health challenge in Indonesia with significant impacts on human resource quality and economic productivity. Accurate prediction of undernourishment prevalence is essential for supporting early warning systems and evidence-based food security policy planning. This study conducted comprehensive comparative analysis of three deep learning architectures—Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer—for predicting Prevalence of Undernourishment (PoU) using longitudinal data from 38 Indonesian provinces spanning 2018-2024 with 242 observations. Methodology encompasses systematic preprocessing with minmaxscaler normalization, 70:15:15 dataset split, implementation of three models with hyperparameter tuning via grid search, and evaluation using RMSE, MAE, R², and MAPE. Results demonstrate Transformer achieves superior performance with RMSE 286.02, MAE 217.79, R² 0.8822, and MAPE 48.11%, outperforming GRU (RMSE 315.19, R² 0.8570) and LSTM (RMSE 356.81, R² 0.8167). Learning curves analysis reveals Transformer exhibits faster convergence and smaller training-validation gap (0.075) compared to LSTM (0.10) and GRU (0.105), indicating superior generalization. Although Transformer exhibits higher computational complexity (125,248 parameters), the accuracy-efficiency trade-off remains favorable with inference time of 8.6ms per sample. Transformer superiority stems from its multi-head self-attention mechanism effectively capturing long-term temporal dependencies and complex non-linear patterns. Findings provide evidence-based recommendations for implementing Transformer in food security early warning systems, supporting targeted resource allocation, and contributing to Sustainable Development Goals achievement related to zero hunger.
Peningkatan Partisipasi Masyarakat dalam Pelaporan Kerusakan Fasilitas Publik di Kota Batam Melalui Aplikasi BALAP-IN Sembiring, Evaliata Br; Wibowo, Ari; Brajawidagda, Uuf; Santiputri, Metta; Mufida, Mir’atul Khusna; Janah, Nur Zahrati; Rokhayati, Yeni; Prasetyaningsih, Sandi; Sartikha, Sartikha; Thohari, Ahmad Hamim; Purnamasari, Dwi Amalia; Uperiati, Alena; Ramadhan, Gilang Bagus; Andesti, Cyntia Lasmi; Afifah, Ummul Fitri; Handayani, Nadya Satya; Simalango, Holong Marisi; Evadini, Sukma
Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Vol. 7 No. 2 (2025): Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/abdimaspolibatam.v7i2.11822

Abstract

Fasilitas public di Kota Batam masih menghadapi sejumlah kendala, seperti kerusakanjalan, taman, mapun lampu penerangan sering kali lambat mendapatkan perbaikan. Rendahnya keterlibatan masyarakat dalam memnyampaikan laporan turut memperburuk kondisi ini, terutama karena mekanisme pelaporan yang dianggap berbelit dan kurang jelas. Salah satu solusi yang dapat dilakukan adalah dengan memanfaatkan teknologi berupa aplikasi BALAP-IN (Batam Lapor-Infrastruktur), dimana aplikasi ini merupakan platform digital yang dirancang untuk membantu masyarakat untuk melaporkan kerusakan fasilitas public secara praktis, transparan, dan terdokumentasi dengan baik. Oleh sebab itu, aplikasi BALAP-IN perlu diperkenalkan kepada masyarakat agar dapat dimanfaatkan sehingga diharapkan tumbuh kesadaran dan partisipasi aktif warga untuk melaporkan kerusakan yang ditemui kepada pihak pengelola. Kegiatan pengabdian menggunakan pendekatan partisipatif dengan menerapkan model teknologi partisipatif. Metode yang digunakan melalui sosialisasi di media sosial dalam bentuk kampanye digital, Brainstorming dan diskusi dengan pihak berwenang, dan FGD. Hasil kegiatan menunjukkan bahwa target dan tujuan tercapai yaitu: (1) Dapat menumbuhkan kesadaran masyarakat mengenai pentingnya melaporkan kerusakan fasilitas publik, melalui partisipasi masyarakat membagikan pengalamannya yang mencapai 70,87%; (2) aplikasi BALAP-IN dikenal masyarakat secara luas dan berpotensi dalam peningkatan partisipasi masyarakat dalam pelaporan kerusakan infrastruktur publik; (3) Antusias masyarakat dalam menggunakan aplikasi BALAP-IN, mengindikasikan dapat meningkatkan jumlah laporan kerusakan fasilitas publik yang valid di kota Batam. Dengan adanya aplikasi ini, diharapkan pengawasan dan pemeliharaan jalan di Kota Batam dapat berjalan lebih responsive, transpran dan berkelanjutan.
Analisa Faktor yang Mempengaruhi Kondisi Kesehatan Menggunakan Algoritma Frequent Pattern Growth Sukma Evadini; Alwis Nazir; Yusra Pizaini
Applied Information System and Management (AISM) Vol. 1 No. 1 (2018): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v1i1.8646

Abstract

Health is an important factor in human life that have to be guarded, both physically and mentally. This study aimed to analyze the factors that affect health condition using medical check up data. Factors analyzed were consuming alcohol, smoking, exercise, age and gender. The method was the association rule using FPGrowth. The result of this study was factors that affect the health condition is alcohol, exercise and age. This result evidenced by the rules A3→K3, which means that if a person consumes more alcohol than 4 days/week with the amount of alcohol is less than 180ml/day, then health condition was poor with 11% support and 67% confidence. E1→K3, which means that if one rarely exercise then health condition was poor with 24% support and 99% confidence. G2→K3, which means that if a person in middle age group, then the condition of health was poor with 24% support and 99% confidence.
Co-Authors Afifah, Ummul Fitri Ahmad Hamim Thohari Alena Uperiati Alwis Nazir Ari Wibowo Cyntia Lasmi Andesti Evaliata Br Sembiring Handayani, Nadya Satya Metta Santiputri Mufida, Mir’atul Khusna Nadya Satya Handayani Nur Zahrati Janah Pizaini Pizaini Purnamasari, Dwi Amalia Ramadhan, Gilang Bagus Rokhayati, Yeni Sandi Prasetyaningsih Sartikha, Sartikha Uuf Brajawidagda