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Impression : Jurnal Teknologi dan Informasi
Published by Lembaga Riset Ilmiah
ISSN : -     EISSN : 29637333     DOI : https://doi.org/10.59086/jti
Core Subject : Science, Engineering,
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering
Impression accepts articles in the fields of Electrical Engineering, Mechanical Engineering, Civil Engineering, Marine Technology Industrial Engineering, Marine Fisheries Technology, Agricultural Technology, Informatics Engineering, Information Systems, Computer, Expert systems, Decision Support Systems, Artificial Intelligence systems, Data Mining, Image processing, Genetic Algorithms, Designing Information Systems, Business Intelligence, Internet of Thing Database System, Big Data, Internet of Thing.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 124 Documents
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Rancang Bangun Sistem Pendaftaran Pasien Berbasis Website (Studi Kasus Pada Klinik Rakyat Merangin) Yanto, Widja; Alhaq, Hawari; Suhada, Nabila Tsalats
Impression : Jurnal Teknologi dan Informasi Vol. 4 No. 3 (2025): November 2025
Publisher : Lembaga Riset Ilmiah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59086/jti.v4i3.1389

Abstract

Transformasi digital pada klinik skala kecil masih menghadapi kendala efisiensi administrasi dan pengelolaan data. Penelitian ini bertujuan merancang dan mengimplementasikan sistem pendaftaran pasien berbasis website di Klinik Rakyat Merangin menggunakan metode Rational Unified Process (RUP) dengan pemodelan UML. Sistem dikembangkan menggunakan framework CodeIgniter, PHP, dan MySQL. Dengan menerapkan sistem pendaftaran berbasis web, klinik dapat menyederhanakan pengelolaan data pasien, mengurangi waktu tunggu, dan meningkatkan efisiensi operasional sebesar 35%. Selain itu, sistem ini berhasil mengurangi kesalahan pencatatan data sebesar 25%, yang sebelumnya terjadi pada proses manual. Hasil implementasi menunjukkan seluruh modul berjalan sesuai spesifikasi berdasarkan pengujian black box dan white box. Secara terukur, waktu pendaftaran menurun dari 10–15 menit menjadi 3–5 menit per pasien (efisiensi ±60–70%) serta mengurangi kesalahan pencatatan melalui validasi otomatis. Evaluasi pengguna menunjukkan peningkatan kemudahan penggunaan dan kepuasan layanan. Sistem ini mendukung digitalisasi layanan kesehatan primer dengan model ringan dan berbiaya rendah. Pengembangan selanjutnya diarahkan pada integrasi rekam medis elektronik, sistem BPJS, serta penguatan keamanan dan skalabilitas sistem.   Digital transformation in small-scale clinics still faces challenges in administrative efficiency and data management. This study aims to design and implement a website-based patient registration system at the Merangin People's Clinic using the Rational Unified Process (RUP) method with UML modeling. The system was developed using the CodeIgniter, PHP, and MySQL frameworks. By implementing a web-based registration system, the clinic can simplify patient data management, reduce waiting times, and increase operational efficiency by 35%. Furthermore, the system successfully reduced data recording errors by 25%, which previously occurred in manual processes. Implementation results showed that all modules ran according to specifications based on black-box and white-box testing. Registration time measurably decreased from 10–15 minutes to 3–5 minutes per patient (efficiency ±60–70%), and recording errors were reduced through automatic validation. User evaluations showed improved ease of use and service satisfaction. This system supports the digitalization of primary healthcare services with a lightweight and low-cost model. Further development is directed at integrating electronic medical records and the BPJS system, as well as strengthening system security and scalability.  
Sinergi Tanggul: Bendungan Drainase Serta Pengolahan Lahan Untuk Pengurangan Resiko Bencana Hidrometeorologi di Sumatera Utara Raegini, Rachels; Hartati Eka Masputri, Sri; Fauzi Nur Ikhsan, Ahmad; Aulia Lubis, Yusuf; Endayanti, Masriani
Impression : Jurnal Teknologi dan Informasi Vol. 4 No. 3 (2025): November 2025
Publisher : Lembaga Riset Ilmiah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59086/jti.v4i3.1392

Abstract

Bencana hidrometeologi, khususnya banjir, merupakan ancaman serius di Indonesia, termasuk Sumatera Utara, yang disebabkan oleh curah hujan ekstrem, deforestasi, urbanisasi dan sistem drainase yang tidak memadai. Penelitian ini bertujuan menganalisis efektivitas sinergi infrastruktur mitigasi banjir, yaitu bendungan, tanggul dan drainase dalam mengurangi resiko bencana di wilayah rawan seperti Medan dan Kabupaten Deli Serdang. Metode penelitian mengunakan data seknder dari Badan Meteorologi, Klimatologi dan Geofisika (BMKG), Badan Nasional Penanggulangan Bencana (BNPB), serta Dinas Pekerjaan Umum dan Pewrumahan Rakyat (PUPR) Sumatera Utara, yang meliputi deskripsi enam bendungan (dengan kapasitas penyimpangan hingga 50 juta m³) dan enam tanggul (dengan panjang 25 km). Simulasi menggunakan ArcGIS dan HEC-RAS membandingkan skenario baseline tanpa intervensi dengan skenario sinergi penuh. Hasil menunjukkan bahwa sinergi infrastruktur dapat mengurangi debit puncak banjir 40% (dari 1.200 m³/detik menjadi 720 m³/detik), area genangan dari 500 km² menjadi 300 km² dan indeks kerentanan dari 0,8 menjadi 0,5. Pendekatan holistik ini terbukti efektif dalam pengelolaan resiko bencana, dengan rekomendasi alokasi 20% APBD untuk pemeliharaan infrastruktur. Namun, keterbaasan penelitian terletak pada cakupan wilayah yang terbatas, sehingga hasil tidak dapat digeneralisasikan untuk seluruh Provinsi Sumatera Utara. Penelitian ini menekankan pentingnya integritasi infrastruktur dan konservasi lahan untuk mitigasi banjir hidrometeorologi.   Hydrometeorological disasters, particularly floods, pose a serious threat in Indonesia, including North Sumatra, caused by extreme rainfall, deforestation, urbanization, and inadequate drainage systems. This study aims to analyze the effectiveness of synergy between flood mitigation infrastructure, namely dams, embankments, and drainage systems, in reducing disaster risks in vulnerable areas such as Medan and Deli Serdang Regency. The research method uses secondary data from the Meteorology, Climatology, and Geophysics Agency (BMKG), the National Disaster Management Agency (BNPB), and the Public Works and Public Housing Agency (PUPR) of North Sumatra, which includes descriptions of six dams (with a storage capacity of up to 50 million m³) and six embankments (with a length of 25 km). Simulations using ArcGIS and HEC-RAS compare a baseline scenario without intervention with a full synergy scenario. The results show that infrastructure synergy can reduce peak flood discharge by 40% (from 1,200 m³/s to 720 m³/s), inundation area from 500 km² to 300 km² and vulnerability index from 0.8 to 0.5. This holistic approach has proven effective in disaster risk management, with a recommendation to allocate 20% of the regional budget (APBD) for infrastructure maintenance. However, the study's limitations lie in its limited coverage area, so the results cannot be generalized to the entire North Sumatra Province. This study emphasizes the importance of infrastructure integrity and land conservation for hydrometeorological flood mitigation.
Prediksi Penyebaran Kasus DBD dengan Pendekatan LSTM di Kota Bangko, Kabupaten Merangin Sari, Rica Syofiana; Sandika, Pio
Impression : Jurnal Teknologi dan Informasi Vol. 4 No. 3 (2025): November 2025
Publisher : Lembaga Riset Ilmiah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59086/jti.v4i3.1408

Abstract

Penyakit Demam Berdarah Dengue (DBD) hingga saat ini masih menjadi tantangan dalam bidang kesehatan masyarakat di Indonesia, termasuk di Kota Bangko, Kabupaten Merangin. Kasus DBD menunjukkan pola musiman dan kadang meningkat secara tiba-tiba, sehingga prediksi dini sangat penting untuk langkah pencegahan. Penelitian ini bertujuan memprediksi jumlah kasus DBD bulanan menggunakan Long Short-Term Memory merupakan varian dari jaringan saraf berulang (Recurrent Neural Network) Data yang digunakan merupakan rekaman historis kasus DBD periode 2019–2022 serta faktor lingkungan seperti curah hujan dan suhu rata-rata. Setelah dilakukan normalisasi dan pembagian data menjadi set pelatihan dan pengujian, model LSTM dilatih untuk memprediksi tren kasus. Hasil penelitian menunjukkan bahwa LSTM mampu menangkap fluktuasi dan pola musiman dengan baik, terlihat dari nilai RMSE yang rendah. Temuan ini dapat dijadikan referensi oleh Dinas Kesehatan Kota Bangko dalam merancang strategi pencegahan dan identifikasi wilayah rawan DBD.   Dengue Fever (DF) remains a significant public health challenge in Indonesia, including in Bangko City, Merangin Regency. DF cases exhibit seasonal patterns and sometimes increase abruptly, making early prediction crucial for preventive measures. This study aims to predict the monthly number of DF cases using Long Short-Term Memory (LSTM), a variant of recurrent neural networks (RNNs). The data used consist of historical DF case records from 2019–2022, along with environmental factors such as rainfall and average temperature. After normalization and splitting the data into training and testing sets, the LSTM model was trained to predict case trends. The results indicate that LSTM effectively captures fluctuations and seasonal patterns, as evidenced by the low RMSE values. These findings can serve as a reference for the Bangko City Health Office in designing preventive strategies and identifying DF-prone areas.  
Studi Komprehensif Faktor Manusia, Kendaraan, Jalan, dan Lingkungan terhadap Kecelakaan Lalu Lintas Indonesia (2018–2023) Sova, Erma; Siahaan, Jonathan Alexander; Harahap, Nanda Dhiarifqi; Assidiqie, Hidarrahman
Impression : Jurnal Teknologi dan Informasi Vol. 5 No. 1 (2026): Maret 2026
Publisher : Lembaga Riset Ilmiah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59086/jti.v5i1.1377

Abstract

Kecelakaan lalu lintas merupakan permasalahan keselamatan publik yang serius di Indonesia karena menimbulkan dampak sosial dan ekonomi yang besar. Penelitian ini bertujuan menganalisis pengaruh faktor manusia, kendaraan, jalan, dan lingkungan terhadap tingkat kecelakaan lalu lintas di Indonesia periode 2018–2023. Penelitian menggunakan pendekatan mixed methods dengan desain explanatory sequential, di mana analisis kuantitatif dilakukan terlebih dahulu untuk mengidentifikasi pola dan hubungan antarvariabel, kemudian diperdalam dengan analisis kualitatif untuk menjelaskan temuan statistik. Data kuantitatif dianalisis menggunakan korelasi Pearson, regresi linier berganda, dan analisis spasial, sedangkan data kualitatif dianalisis secara tematik. Hasil penelitian menunjukkan bahwa faktor manusia merupakan penyumbang terbesar kecelakaan lalu lintas dengan kontribusi 81,5%, diikuti oleh faktor jalan (40%), kendaraan (28%), dan lingkungan. Analisis regresi menunjukkan bahwa keempat faktor tersebut secara simultan berpengaruh signifikan terhadap tingkat kecelakaan dengan nilai koefisien determinasi (R²) sebesar 0,72. Temuan ini menegaskan secara empiris pengaruh terpadu keempat faktor dalam kerangka sistem keselamatan jalan (Safe System). Implikasi kebijakan penelitian ini menekankan pentingnya prioritas intervensi keselamatan berbasis faktor dominan melalui peningkatan edukasi dan penegakan hukum, pengawasan kelaikan kendaraan, serta perbaikan infrastruktur pada titik rawan kecelakaan berbasis data.   Traffic accidents represent a serious public safety issue in Indonesia, generating significant social and economic impacts. This study aims to analyze the influence of human, vehicle, road, and environmental factors on the level of traffic accidents in Indonesia during the 2018–2023 period. The research employs a mixed methods approach with an explanatory sequential design, where quantitative analysis is conducted first to identify patterns and relationships among variables, followed by qualitative analysis to further explain the statistical findings. Quantitative data were analyzed using Pearson correlation, multiple linear regression, and spatial analysis, while qualitative data were examined through thematic analysis. The results indicate that human factors are the dominant contributor to traffic accidents, accounting for 81.5%, followed by road factors (40%), vehicle factors (28%), and environmental factors. Regression analysis shows that these four factors simultaneously have a significant effect on traffic accident levels, with a coefficient of determination (R²) of 0.72. These findings empirically confirm the integrated influence of the four factors within the road safety Safe System framework. The policy implications highlight the need to prioritize safety interventions based on dominant factors, including enhanced education and law enforcement, stricter vehicle roadworthiness supervision, and data-driven improvements to infrastructure at accident-prone locations.  

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