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Teknika
Published by Institut Informatika Indonesia Surabaya
ISSN : 25498037     EISSN : 25498045     DOI : https://doi.org/10.34148/teknika
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
Teknika is a peer-reviewed journal dedicated to disseminate research articles in Information and Communication Technology (ICT) area. Researchers, lecturers, students, or practitioners are welcomed to submit paper which has topic below: Computer Networks Computer Security Artificial Intelligence Machine Learning Human Computer Interaction Computer Vision Virtual/Augmented Reality Digital Image Processing Data Mining Web Mining Computer Architecture Software Engineering Decision Support System Information System Audit Business Information System Datawarehouse & OLAP And any other topics relevant with Information and Communication Technology (ICT) area
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 276 Documents
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Klasterisasi Data Obat Farmasi Berdasarkan Jumlah Persediaan Dengan Menggunakan Metode K-Means Supriyanto, Heri; Hafidz, Mohammad Al; Puspitaningrum, Ari Cahaya; Firmansyah, Rayhan Abdillah Putra; Zuhdi, Rafi
Teknika Vol. 13 No. 3 (2024): November 2024
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v13i3.987

Abstract

Instalasi Farmasi memiliki peran penting terhadap pelayanan kesehatan di sebuah fasilitas kesehatan. Farmasi bertanggung jawab atas pengelolaan, pengadaan, penyimpanan, distribusi, dan penggunaan persediaan obat. Persediaan obat merupakan bagian penting dalam memastikan ketersediaan, aksesibilitas, dan penggunaan obat yang efektif serta aman bagi pasien. Tujuan penelitian ini untuk melakukan klasterisasi data obat yang berguna untuk meningkatkan efisiensi proses manajemen persediaan obat, sehingga dapat menghindari kelebihan atau kekurangan yang dapat mengganggu kelancaran layanan pemberian obat dan mencegah terjadinya kerugian penjualan obat. Pengelompokan data dilakukan dengan memanfaatkan data Persedian Obat dari data masa lalu yaitu data transaksi pembelian dan penjualan dengan memanfaatkan teori Data Mining dengan menggunakan metode Clustering yaitu K-Means. Dataset pada penelitian ini sebanyak 1.389 dengan 6 variabel. Sebelum dilakukan klasterisasi dilakukan proses optimasi jumlah klaster dengan dua metode yaitu Metode Elbow dan Metode Gap Statistik. Hasil kedua metode tersebut menunjukkan nilai optimasi k klaster k = 3. Hasil klasteriasi yaitu Klaster 1 sebanyak 41 data obat yang menunjukkan golongan obat Generik. Klaster 2 sebanyak 116 data obat yang menunjukkan obat Paten. Kedua klaster tersebut menunjukkan tingkat penjualan yang kurang cepat (slow moving). Sedangkan pada Klaster 3 sebanyak 1.232 data obat yang menunjukkan gabungan dari golongan obat generik dan paten yang memiliki tingkat penjualan yang cukup cepat (fast moving).
Pengembangan Model Klasifikasi Kendaraan Keluar Masuk Area Parkir Dengan Algoritma YOLOv8 Faturrohman, Argi Nur; Suryawan, Sayekti Harits; Rahim, Abdul
Teknika Vol. 13 No. 3 (2024): November 2024
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v13i3.992

Abstract

Peningkatan laju pertumbuhan mahasiswa baru menimbulkan tantangan serius terhadap infrastruktur parkir di Universitas Muhammadiyah Kalimantan Timur (UMKT). Data terkini menunjukkan adanya peningkatan signifikan sekitar 10% dari tahun sebelumnya, mencapai 2.598 mahasiswa baru pada tahun 2022. Ruang lingkup penelitian ini adalah melakukan proses klasifikasi kendaraan tetapi tidak melakukan tracking kendaraan, data yang digunakan adalah data dari perekaman video yang dilakukan pada simpang tanjakan menuju area parkir kampus bagian atas di siang hari, serta objek yang dideteksi adalah motor, mobil dan manusia, sedangkan yang dihitung keluar masuknya adalah mobil dan motor. Tujuan penelitian ini adalah mengimplementasikan algoritma YOLOv8 agar dapat mendeteksi serta mengklasifikasikan kendaraan keluar masuk area parkir serta untuk mengetahui bagaimana proses deteksi dapat diterapkan agar dapat akurat untuk mendeteksi kendaraan yang keluar masuk area parkir. Metode penelitian melibatkan pengumpulan data dan penerapan algoritma YOLOv8 (You Only Look Once) untuk training dan validasi model pada platform Google Colab yang mendukung GPU untuk mempercepat komputasi dan memungkinkan pengolahan data dalam skala besar. Hasil dari penelitian ini adalah model klasifikasi yang dapat mendeteksi kendaraan keluar masuk area parkir UMKT dengan memiliki nilai mAP50 sebesar 89,8% dan nilai presisi sebesar 86,5%. Penelitian selanjutnya diharapkan dapat mengembangkan model dengan tingkat akurasi yang lebih tinggi dengan mengintegrasikan CCTV sebagai sumber video secara real-time.
Analisis Sentimen Ulasan Game Stumble Guys Pada Playstore Menggunakan Algoritma Naïve Bayes Nurdy, Awang Herjunie; Rahim, Abdul; Arbansyah
Teknika Vol. 13 No. 3 (2024): November 2024
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v13i3.993

Abstract

Perkembangan teknologi yang pesat mempermudah akses ke berbagai hiburan digital, termasuk game online seperti Stumble Guys, yang telah diunduh lebih dari 163 juta kali dan mendapatkan ulasan beragam di Google Play Store. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna Stumble Guys menggunakan algoritma Naïve Bayes. Metode penelitian melibatkan tahapan Knowledge Discovery in Databases (KDD), meliputi pemilihan data, preprocessing, transformasi dengan CountVectorizer dan TF-IDF, serta pengklasifikasian dengan Naïve Bayes. Dengan menggunakan 1.500 ulasan dari Google Play Store, model Naïve Bayes mencapai akurasi 86%, dengan precision, recall, dan f1 score masing-masing sebesar 86%. Hasil penelitian menunjukkan bahwa Naïve Bayes efektif dalam mengklasifikasikan sentimen ulasan game Stumble Guys.
Analisis Faktor Yang Mempengaruhi Kepuasan Pengguna Media Sosial X Menggunakan Metode End User Computing Satisfaction (EUCS) Rahayu, Flourensia Sapty; Pritalia, Generosa Lukhayu; Kurniawan, Felix
Teknika Vol. 13 No. 3 (2024): November 2024
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v13i3.1006

Abstract

Aplikasi media sosial adalah salah satu teknologi berbasis internet yang menghubungkan masyarakat dalam mengekspresikan diri, melakukan kolaborasi, dan menyebarkan serta memperoleh data informasi secara digital. Beberapa tahun belakangan ini, aplikasi media sosial Twitter menuai kontroversi akibat diakuisisi dan melakukan rebranding menjadi X dimana merupakan aplikasi microblogging yang memungkinkan penggunanya dalam mengunggah status atau opini terkait suatu objek atau fenomena, menyiarkan berita, periklanan, hingga isu politik. Sampai saat ini, X belum diketahui telah terbukti mampu memberikan kepuasan terhadap penggunanya dimana akan bermanfaat apabila hal tersebut dievaluasi. Penelitian ini bertujuan untuk mengidentifikasi faktor-faktor yang berkontribusi terhadap tingkat kepuasan pengguna aplikasi media sosial X dengan End-User Computing Satisfaction (EUCS) sebagai teori dan model penelitian. Metodologi dari penelitian ini menggunakan pendekatan kuantitatif dengan melibatkan 384 responden. Data dari responden diolah dan dianalisa dengan bantuan software Statistical Product and Service Solutions (SPSS). Hasil penelitian menunjukkan responden pengguna aplikasi media sosial X merasakan kepuasan yang cukup baik dimana setiap faktor dari EUCS yaitu konten, akurasi, tampilan, kemudahan penggunaan, serta ketepatan waktu secara signifikan dan parsial mempengaruhi kepuasan pengguna aplikasi media sosial X.
Precision in Obstetric Care: A Machine Learning Approach with CatBoost and Grid Search Optimization Hiswati, Marselina Endah; Diqi, Mohammad; Azijah, Izattul; Subandi, Yeyen; Fathinah, Azzah; Ariani, Rahayu Cahya
Teknika Vol. 13 No. 3 (2024): November 2024
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v13i3.1010

Abstract

This study focuses on improving how we classify fetal health using machine learning by fine-tuning the CatBoostClassifier with Grid Search. Our main achievement in this research is significantly boosting the accuracy of fetal health classification based on Cardiotocogram (CTG) data. Finding the best hyperparameters has created a more precise and reliable diagnostic tool for making informed prenatal care decisions. The model reached an impressive overall accuracy of 96%, especially excelling in identifying Normal and Pathological cases. However, it faced some challenges in classifying Suspect cases, suggesting room for further improvement. These results highlight the potential of machine learning to enhance the reliability of fetal health assessments, which could lead to better outcomes in clinical settings. The success of Grid Search in this study is evident, as the optimized parameters led to the highest accuracy and lowest loss values, proving its effectiveness in fine-tuning the model.
Penerapan Metode Simple Additive Weighting (SAW) Dalam Pemilihan Supplier Terbaik Pada Industri Manufaktur Muttaqin, Zaenul; Handayani, Dini; Triyono, Gandung
Teknika Vol. 13 No. 3 (2024): November 2024
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v13i3.1024

Abstract

Memilih supplier didalam rantai pasok industri manufaktur merupakan hal yang sangat penting untuk keberhasilan operasional dan daya saing perusahaan, karena mempengaruhi biaya, efisiensi, kualitas produk, kepuasan pelanggan, dan reputasi perusahaan. Dalam konteks globalisasi dan kompleksitas rantai pasok yang meningkat, berbagai kriteria seperti kualitas produk, ketepatan waktu pengiriman, dan kepatuhan terhadap standar internasional seperti International Organization for Standardization (ISO) 22000:2018 dan ISO 9001:2015 harus dipertimbangkan untuk memastikan pemenuhan kebutuhan jangka pendek serta keberlanjutan operasional jangka panjang. Penelitian ini bertujuan mengatasi masalah pemilihan supplier terbaik dengan menerapkan metode Simple Additive Weighting (SAW), yang efisien dalam mengevaluasi kriteria relevan. Langkah awal melibatkan identifikasi kriteria penilaian yang relevan seperti kualitas, waktu pengiriman, dan kepatuhan standar ISO, kemudian memberikan bobot pada setiap kriteria berdasarkan tingkat kepentingannya. Data kinerja supplier dikumpulkan secara sistematis dan diproses melalui metode Simple Additive Weighting (SAW) untuk menghasilkan peringkat relatif dari setiap supplier. Hasil penelitian menunjukkan supplier V7 memperoleh peringkat tertinggi dengan nilai 50,8, memberikan kontribusi berharga dalam pemahaman dan pemilihan supplier terbaik dalam konteks keandalan, kualitas, dan kepatuhan standar industri, serta menunjukkan bahwa penggunaan metode Simple Additive Weighting (SAW) meningkatkan efisiensi perhitungan dan pengelolaan data.
Optimizing Tourism Promotion for Situ Bagendit Through Innovation in a Web-Based Virtual Tour Application Rahayu, Sri; Yanuar, Syahrul; Bustomi, Yosep
Teknika Vol. 13 No. 3 (2024): November 2024
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v13i3.1037

Abstract

Situ Bagendit is a well-known natural lake tourist destination in Garut, West Java. However, information about Situ Bagendit is still difficult for the public to access. Information is often spread by word of mouth and social networks, which highlights the need for more effective promotional media. This research aims to create a virtual tour to promote Situ Bagendit and address the issue of information accessibility for tourists. The application was developed using the Multimedia Development Life Cycle (MDLC) method, utilizing VR technology and 3DVISTA software, and incorporating images and videos captured with a mobile or 360° camera. The application is hosted on Instagram, featuring interactive elements such as chat and location information via Google Maps. The research findings indicate that the virtual tour application was successfully built with features like a gallery, videos, information, WhatsApp, and Google Maps. It received a score of 80.25 on the System Usability Scale (SUS), earning an "Excellent" rating and falling within the "Acceptable" category. This application is expected to increase tourist interest in visiting Situ Bagendit.
Analysis of LoRaWAN Network Signal Coverage and Quality Parameters in Real-Time: Case Study of Cikumpa River Water Quality Monitoring, Depok City Ariansa, Hasri; Pratama, Legenda Prameswono; Faizah, Safira; Putri, Arisa Olivia; Jaenul, Ariep; Dionova, Brainvendra Widi; Al-Humairi, Safaa Najah Sahud; Mohammed, M. N.
Teknika Vol. 13 No. 3 (2024): November 2024
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v13i3.1060

Abstract

In the context of an increasingly advanced era, Internet of Things (IoT) technology has emerged as a significant innovation across a range of fields. One of the most rapidly developing Internet of Things (IoT) technologies is the Long Range Wide Area Network (LoRaWAN). LoRaWAN is capable of long-distance communication while simultaneously consuming minimal power. In this study, we analyze the coverage of the LoRaWAN network in transmitting data with Cikumpa river water objects, with a 100–600 meters distance between the transmitter (TX) and receiver (RX). This study assesses the RSSI network quality, LoRaWAN SNR, and LoRaWAN network QoS quality concerning throughput, delay, jitter, and packet loss parameters. The testing results demonstrated that the LoRaWAN network coverage reaches a maximum distance of 600 meters. Researchers conducted the testing in the Cikumpa River area. They then analyzed the RSSI and SNR test results in the morning, afternoon, and evening. The results of the RSSI test and calculations demonstrate that as the distance between the transmitter and receiver increases, the RSSI value decreases. The RSSI testing conducted in the morning exhibited a range of -99 dBm to -121 dBm, with the SNR values spanning from -3.25 dB to 8.75 dB. The results of the daytime RSSI tests ranged from -104 dBm to -124 dBm, with the corresponding SNR values ranging from -8.50 dB to 9.00 dB. The RSSI test results for the afternoon period exhibited a range of -96 dBm to -120 dBm, while the SNR demonstrated a range of -7.25 dB to 9.00 dB. In addition, the quality of service (QoS) can be considered stable based on the results of the RSSI and SNR for each test. During the testing process, conducted at distances between 100 and 600 meters, there was no packet loss when data transmission occurred. This research demonstrates the potential for utilizing LoRaWAN technology to monitor a desired object remotely.
Design and Construction of a Web-Based Fixed Asset Management System with a Combination of Straight Line Method, MAUT, and Telegram Bot Integration: Case Study of North Lombok District Hospital Arthana, I Made Teguh; Wirdiani, Ni Kadek Ayu; Putri, Desy Purnami Singgih
Teknika Vol. 13 No. 3 (2024): November 2024
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v13i3.1061

Abstract

North Lombok District Hospital is a health service institution in North Lombok District, West Nusa Tenggara Province, that provides health facilities and services to the community. Health service facilities provided to the community come from fixed assets owned by the North Lombok District Hospital. Management of fixed assets used for health service facilities at the North Lombok District Hospital is still done manually in planning, receiving, repairing, maintaining, and releasing assets. So, hospital employees have difficulty managing the assets they own. This study was conducted to help design and build a fixed asset management information system at the North Lombok Hospital using the SDLC Method with the Waterfall Model approach and system development using PHP, HTML, CSS, and JS languages with the Laravel Framework and MYSQL Database. This study uses the Straight Line Method to calculate asset depreciation, the MAUT Method to assist in decision-making for the elimination of damaged assets, and the Telegram Bot to send notifications from the website to each unit group in the hospital. The final result of this study is a web-based fixed asset management information system with developed features, namely asset planning features, asset planning change features, asset handover minutes features, asset inventory features, asset maintenance features, asset repair features, asset write-off features, asset whitening features, asset reporting features, master data features, and user access rights management features. The testing method used in this study is the Blackbox testing method, which tests the functionality of the system using 150 test scenarios on eight employees of the North Lombok Regional Hospital, with the test results showing that the system is running well and in accordance with the SOP that has been given, PSSUQ testing was carried out to evaluate user satisfaction with the system. The test results showed a SysUse subscale value of 1.93, IntQual 1.6, InfoQual 1.92, and Overall 1.93. Based on the results of the PSSUQ test, it can be concluded that the fixed asset management system has run very well and meets user expectations.
Comparison of Deep Neural Networks and Random Forest Algorithms for Multiclass Stunting Prediction in Toddlers Lestari, Wulan Sri; Saragih, Yuni Marlina; Caroline
Teknika Vol. 13 No. 3 (2024): November 2024
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v13i3.1063

Abstract

Stunting in toddlers is a serious global health issue, with long-term impacts on physical growth and cognitive development. To address this problem more effectively, it is crucial not only to identify whether a child is stunted but also to predict the severity of the condition. Multiclass stunting prediction offers deeper insights into a child’s condition, enabling more precise and targeted interventions. This study aims to compare the performance of multiclass stunting prediction models using two machine learning algorithms: Deep Neural Networks and Random Forest. The research process involved data collection, preprocessing, as well as model development and testing. The results show that the Random Forest model achieved 100% accuracy in training and 99.92% accuracy in testing, while the Deep Neural Networks model achieved 93.49% accuracy in training and 93.21% in testing. Both models demonstrated strong performance in multiclass stunting prediction, with Random Forest proving superior in terms of accuracy compared to Deep Neural Networks.

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