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All Journal snimed Jurnal Teknologi Informasi dan Ilmu Komputer Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Journal of Information System and Technology (JOINT) Jurnal Ilmiah Teknik Informatika dan Komunikasi Jurnal Salome: Multidisipliner Keilmuan VISA: Journal of Vision and Ideas Journal of Innovative and Creativity
Rizki Surtiyan Surya
Universitas Muhammadiyah Pontianak
Religion Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Pengembangan Sistem Informasi Manajemen Rawat Inap Puskesmas Ngemplak 1 Sleman Surya, Rizki Surtiyan; Kurniawan, Rahadian; Muhimmah, Izzati
Seminar Nasional Informatika Medis (SNIMed) 2017
Publisher : Magister Teknik Informatika, Universitas Islam Indonesia

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Abstract

Pusekesmas Ngemplak I Sleman adalah salah satu puskesmas rawat inap yang ada di Kabupaten Sleman. Rawat inap di puskesmas Ngemplak I Sleman masih melakukan proses pencatatan rekam medis secara manual. Ada beberapa masalah yang muncul jika pencatatan dilakukan secara manual, seperti: memerlukan tempat atau ruangan yang besar dalam menyimpan data rekam medis pasien, pencarian data rekam medis pasien menjadi lama ketika banyaknya data rekam medis pasien, jika terjadi kesalahan pada pencatatan rekam medis pasien harus mengganti dengan yang baru. Pada penelitian ini akan dibahas mengenai proses pengembangan Sistem Informasi Manajemen Rawat Inap Puskesmas Ngemplak I. Sistem dikembangkan berbasis web dan dibangun dengan bahasa pemrograman PHP. Dari hasil pengujian sistem disimpulkan, bahwa tampilan sistem informasi rawat inap ini sudah cukup baik. Kemudian, data-data yang dimasukkan berdasarkan form perawatan sudah cukup sesuai dengan pelayanan yang ada di Puskesmas, data pasien, dan data rekam medis pasien lebih tersusun rapi serta pencarian informasi data pasien, dan rekam medis pasien lebih cepat.
Klasifikasi Land Surface Temperature Spasial-Temporal di Kalimantan Barat Menggunakan Metode Naïve Bayes dan C4.5 Hidayah, Miftah; Utami, Putri Yuli; Surya, Rizki Surtiyan
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 13, No 3: Desember 2024
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v13i3.2311

Abstract

This study aims to determine the performance level and classification results of land surface temperature with climate, as they mutually influence each other and undergo significant changes that impact the environment. The research uses data mining to classify the patterns of LST changes using spatial data and climate attribute data that affect LST in four regencies in West Kalimantan during the peak dry season of the last five years, comparing the Naïve Bayes and C4.5 algorithms. The data used comes from the USGS and the West Kalimantan Climate Station, covering 11 variables: latitude, longitude, maximum temperature, average temperature, average humidity, precipitation, solar radiation, and average wind speed, time, region and labels. Data preprocessing was conducted to facilitate data processing, and the algorithms were applied using RapidMiner tools. The results show that the Naïve Bayes algorithm has an accuracy of 94.57%, while C4.5 achieves 99.22%. This study can serve as a basis for enhancing understanding and planning climate change adaptationKeywords: C4.5 Algirithm; Classification; Land Surface Temperature; Naïve Bayes Algorithm; Prediction AbstrakPenelitian ini bertujuan untuk mengetahui tingkat kinerja dan hasil klasifikasi land surface temperature dengan iklim karena saling memengaruhi dan mengalami perubahan signifikan yang berdampak terhadap lingkungan. Penelitian menggunakan data mining untuk mengklasifikasi pola perubahan LST menggunakan data spasial dan data atribut iklim yang memengaruhi LST pada empat kabupaten di Kalimantan Barat pada puncak kemarau 5 tahun terakhir menggunakan perbandingan algoritma Naïve Bayes dan C4.5. Data yang digunakan berasal dari USGS dan Stasiun Iklim Kalimantan Barat yang mencakup 11 variabel yaitu latitude, longitude, temperatur maximall, rata-rata temperature, rata-rata kelembapan, intensitas hujan, penyinaran matahari, dan rata-rata kecepatan angin, waktu, wilayah dan label. Pre-proses data dilakukan memudahkan pengolahan data, penerapan algoritma menggunakan tools RapidMiner. Hasil menunjukkan bahwa penggunaan algoritma Naïve Bayes memiliki accuracy sebesar 94,57%, sedangkan C4.5 sebesar 99.22%. Penelitian ini dapat menjadi dasar dalam meningkatkan pemahaman dan dalam perencanaan adaptasi perubahan iklim. 
PERANCANGAN UI/UX ADHA KOST MENGGUNAKAN METODE DESIGN THINKING Fathan Fakhrana Auafar; Putri Yuli Utami; Rizki Surtiyan Surya
Jurnal Salome : Multidisipliner Keilmuan Vol. 3 No. 1 (2025): Januari
Publisher : CV. ADIBA AISHA AMIRA

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Abstract

The development of the boarding house business in the city of Pontianak is increasing, but this does not necessarily mean that potential consumers can easily find boarding houses. Information obtained from various media created by boarding house owners, such as uploading photos and videos on social media platforms such as Facebook, Instagram and others, is considered to be less effective. The design thinking method is a collaborative method that collects many ideas to obtain a solution. Design Thinking has comprehensive thinking to get a solution with 5 method stages, namely empathize, define, ideate, prototype and test, with this method you can solve existing problems. To connect boarding house owners and potential consumers, one way is to create a website-based application related to the availability of boarding rooms to make it easier for boarding house seekers to find boarding houses and boarding house owners to market boarding houses. To make the website usable well requires a website design that can be easily accessed or used either as a boarding house seeker or boarding house owner. Designing using the Design Thinking method makes it easier to create UI / UX for Adha Kost. Apart from that, designing the design using the Design Thinking method makes the design look more attractive and the items used are more complete in accordance with the user's needs.
Prediksi Produktivitas Tanaman Biofarmaka Di Kalimantan Barat Menggunakan Algoritma Backpropagation Wahyudi, Wahyudi; Utami, Putri Yuli Utami; Surya, Rizki Surtiyan
Journal of Innovative and Creativity Vol. 5 No. 2 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i2.2059

Abstract

Indonesia dikenal sebagai negara dengan keanekaragaman hayati terbesar di dunia, salah satunya adalah tanaman biofarmaka yang memiliki potensi besar dalam pengembangan obat-obatan alami. Kalimantan Barat merupakan salah satu provinsi yang kaya akan keanekaragaman hayati, terutama terkait tanaman biofarmaka. Namun, produksi tanaman biofarmaka di provinsi ini mengalami fluktuasi setiap tahunnya, yang dapat memengaruhi kesejahteraan petani dan ketersediaan obat tradisional bagi masyarakat. Penelitian ini bertujuan untuk memprediksi produktivitas tanaman biofarmaka di Kalimantan Barat menggunakan algoritma backpropagation. Berbagai model jaringan saraf tiruan diuji untuk mengidentifikasi model yang paling akurat dalam memprediksi produktivitas tanaman biofarmaka dari tahun 2024 hingga 2028. Hasilnya menunjukkan bahwa model arsitektur 9-20-1 adalah yang terbaik, dengan nilai mean squared error (MSE) sebesar 0,00269169 dan akurasi 85,7%. Prediksi menunjukkan bahwa jahe dan kunyit mengalami peningkatan umum meskipun ada fluktuasi, kencur tumbuh konsisten, laos/lengkuas mengalami penurunan bertahap, mahkota dewa terus meningkat setiap tahun, mengkudu fluktuatif, dan temulawak tumbuh stabil dengan peningkatan konsisten hingga 2028.
Pemodelan Kausal Faktor-Faktor Beban Keluarga dalam Merawat Pasien Kanker Menggunakan Algoritma S3C-Latent Surya, Rizki Surtiyan; Effendy, Christantie; Rahmadi, Ridho
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 1: Februari 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.0814266

Abstract

Pasien kanker memiliki kebutuhan yang kompleks mulai dari masalah fisik, psikologis, sosial dan spiritual. Keluarga yang merawat pasien kanker disebut family caregiver. Seorang family caregiver membantu mengatasi hampir semua permasalahan yang dialami pasien baik saat dirawat di rumah maupun di rumah sakit. Keluarga mengalami suka dan duka dalam merawat pasien. Dalam merawat pasien dengan penyakit kronis, bukan hanya pasien tetapi kesejahteraan dan kualitas hidup family caregiver juga penting. Oleh karena itu sangat penting untuk mengetahui bagaimana beban family caregiver dan faktor-faktor yang mempengaruhi beban keluarga dalam merawat pasien.  Beban family caregiver dapat diukur menggunakan Caregiver Reaction Assesment (CRA), yang direpresentasikan oleh beberapa faktor. Dengan memahami hubungan kausal antara faktor-faktor beban keluarga, diharapkan dapat membantu untuk mengidentifikasi bagaimana beban caregiver bersumber dan berdampak. Untuk itu, penelitian ini bertujuan untuk mengidentifikasi hubungan kausal antara faktor-faktor yang berhubungan dengan beban family caregiver dalam merawat pasien. Penelitian ini menggunakan algoritma pemodelan kausal bernama Stable Specification Search for Cross-sectional Data with Latent Variable (S3C-Latent) untuk mendapatkan model kausal antara faktor-faktor beban family caregiver yang relevan. Dari hasil analisis  pemodelan  didapatkan ada 3 faktor yang memiliki hubungan kausal dan 2 faktor memiliki hubungan asosiasi. Gender memiliki hubungan kausal yang stabil terhadap kesiapan kesehatan dan kesiapan dalam merawat. Sedangkan faktor kesiapan merawat mempengaruhi faktor aktivitas family caregiver, selain itu faktor keuangan memiliki hubungan asosiasi yang kuat dengan faktor aktivitas dan hubungan keluarga. Pemodelan kausal ini dapat digunakan sebagai acuan bagi tenaga kesehatan dalam pelayanan kesehatan yang lebih tepat, efisien, dan efektif di dalam menangani permasalahan beban caregiver. AbstractCancer patients have complex needs ranging from physical, psychological, social, and spiritual problems. Families who take care for cancer patients are called family caregivers. A family caregiver helps to overcome almost all problems experienced by the patients both while being treated at home and in the hospital. Families experience joy and sorrow in caring for patients. In treating patients with chronic diseases, not only the patient but the family caregiver's well-being and quality of life are also important. Therefore, it is very important to know how the family caregiver's burden is and the factors that affect the family burden in caring for patients. Caregiver family burden can be measured using Caregiver Reaction Assessment (CRA), which is represented by several factors. By understanding the causal relationship between family burden factors, it is hoped that it can help to identify how the caregiver burden is sourced and impacted. Therefore, this study aims to identify the causal relationships between factors related to the burden on family caregivers in caring for patients. This study uses a causal modeling algorithm called Stable Specification Search for Cross-sectional Data with Latent Variable (S3C-Latent) to obtain a causal model between the relevant caregiver family load factors. The results of modeling analysis showed that there are 3 factors which have a causal relationship and 2 factors have an association relationship. Gender has a stable causal relationship to health readiness and readiness to care, Moreover, the caring readiness factor affects the family caregiver activity factor, and the financial factor has a strong association with the activity factor and family relationships. This causal modeling can be used as a reference for health workers so as to give health services which are precise, efficient, and effective in dealing with caregiver burden problems.
Strategi Pengembangan Potensi Ekowisata Mangrove Polaria Tanjung Pagar Menjadi Produk Wisata Kreatif Menggunakan Metode Analisis SWOT dan Analytic Hierarchy Process (AHP) Istikoma, Istikoma; Surya, Rizki Surtiyan
VISA: Journal of Vision and Ideas Vol. 5 No. 1 (2025): Journal of Vision and Ideas (VISA)
Publisher : IAI Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/visa.v5i1.6639

Abstract

Mendalok Village has a tourist area called the Tanjung Pagar Polaria Mangrove Ecotourism. The ecotourism is no longer developing due to the impact of Covid 19 and coastal abrasion so that it is not managed optimally and has an impact on the tourist location being deserted by visitors. Four criteria form the analysis using the Analytic Hierarchy Process (AHP). The ranking is based on how much each aspect influences the growth of mangrove ecotourism, and the objectives and weights are determined using objectives, criteria, and alternatives. The results of the scoring are obtained from multiplying the weight by the rating resulting in a score on the criteria of Improving Community Living Standards (MTHM) of 0.82%, Mangrove Ecosystem Sustainability (KEM) of 0.26% and Human Resources (HR) 0.06% and Creating Jobs (MLK). External components (opportunities and threats) and internal (strengths and weaknesses) of the project or organization are evaluated using the SWOT method. According to the study, there is a physical carrying capacity of 59 individuals per day and the mangrove ecotourism area has the potential to grow on an area of 10 hectares. Ten hours have been allocated for operations by management. 
Prediksi Tingkat Produksi Panen Buah Jeruk Siam Dengan Metode Regresi Linear Sederhana Ridwan, Ridwan; Istikoma, Istikoma; Surya, Rizki Surtiyan
Journal of Information System and Technology (JOINT) Vol. 6 No. 3 (2025): Journal of Information System and Technology (JOINT)
Publisher : Program Sarjana Sistem Informasi, Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/joint.v6i3.11405

Abstract

The production of Siam oranges in Sambas Regency has experienced fluctuations, with the highest peak production recorded in 2019 at 123,739 tons, but declining to 88,119 tons in 2023 based on data from the Department of Food Crops and Horticulture of West Kalimantan Province. Additionally, the highest harvest production in 2019 reached 123,739 tons, while in 2021, the lowest production was recorded at 51,691 tons. This decline was largely caused by heavy rainfall and floods that damaged agricultural land. The simple linear regression method produced a regression equation indicating a significant relationship between harvested area (independent variable) and orange production (dependent variable). The results of the analysis using simple linear regression calculations produced a total RMSE value of 17321.364 for 18 sub-districts. This demonstrates that the regression model is effective in predicting orange production in Sambas Regency, considering historical data and the harvested area from 18 sub-districts. This model is expected to assist in planning and decision-making regarding orange production in the future.
Perbandingan Algoritma Random Forest dan Decision Tree untuk Memprediksi Cuaca di Kalimantan Barat Nur Ayu Fitri Maharani; Putri Yuli Utami; Rizki Surtiyan Surya
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 5 No. 3 (2025): November: Jurnal Ilmiah Teknik Informatika dan Komunikasi 
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v5i3.1639

Abstract

This research aims to determine the results of weather predictions in West Kalimantan. This research was conducted to compare the Random Forest and Decision Tree algorithms in predicting weather in West Kalimantan. The data used comes from the BMKG West Kalimantan Climate Station from 2022-2024 including attributes, namely, date, average temperature (Tavg), average humidity (RH_avg), rainfall (RR), duration of sunlight (ss), and average wind speed (ff-avg). The initial stage of research using the CRISP-DM method is to collect climate data obtained from the official website of the Meteorology, Climatology and Geophysics Agency (BMKG), then process the data, normalize the data, and then apply the Random Forest and decision tree algorithms using the RapidMiner tool. . Based on the results of weather prediction research with a comparison of Random Forest algorithm test data with an accuracy of 94.64% and Random Forest algorithm training data with an accuracy of 94.05%. And to compare the decision tree algorithm test data with an accuracy of 93.45% and the decision tree algorithm training data with an accuracy of 92.26%.
Co-Authors Christantie Effendy Fathan Fakhrana Auafar Hidayah, Miftah Istikoma Istikoma Izzati Muhimmah Nur Ayu Fitri Maharani Putri Yuli Utami Rahadian Kurniawan Ridho Rahmadi Ridwan, Ridwan Utami, Putri Yuli Utami, Putri Yuli Utami Wahyudi Wahyudi