OJS Universitas Mulia (E-Journals)
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Analisis Tingkat Kepuasan Antara Pasien BPJS Dengan Pasien Umum Terhadap Pelayanan Kefarmasian di Klinik Panacea Kota Balikpapan
No full textPelayanan Kesehatan adalah bentuk kegiatan dan serangkaian kegiatan pelayanan yang diberikan secara langsung kepada masyarakat untuk memelihara dan meningkatkan derajat kesehatan masyarakat dalam bentuk promotif, preventif, kuratif, rehabilitatif dan paliatif. Penelitian ini bertujuan untuk menganalisis tingkat kepuasan antara pasien BPJS dengan pasien umum terhadap pelayanan kefarmasian di Klinik Panacea Kota Balikpapan. Jenis penelitian yang digunakan adalah observasional analitik dengan desain penelitian cross-sectional. Teknik pengambilan sampel menggunakan teknik purposive sampling dengan instrument penelitian berupa kuesioner yang terlebih dahulu telah diuji validitas dan uji reliabilitas. Berdasarkan hasil penelitian yang telah dilakukan dengan menggunakan 5 indikator pelayanan kefarmasian yakni aspek bukti fisik (Tangible) didapat hasil pada pasien BPJS 78% dan pasien umum 80%, aspek kehandalan (Reliability) diperoleh hasil pada pasien BPJS 78% dan pasien umum 80%, aspek daya tanggap (Responsiveness) diperoleh hasil pada pasien BPJS 76% dan pasien umum 81 %, aspek jaminan (Assurance) diperoleh hasil pada pasien BPJS 76% dan pasien umum 80% dan terakhir aspek empati (Emphaty) diperoleh hasil pada pasien BPJS 77% dan pasien umum 82%. Hasil penelitian ini dapat disimpulkan bahwa tingkat kepuasan pasien BPJS mendapatkan nilai rata - rata sebesar (77%) dan pasien umum (80%) dengan kategori puas terhadap pelayanan kefarmasian di Klinik Panacea.
Kata Kunci : Tingkat Kepuasan Pasien BPJS dan pasien umum, Pelayanan Kefarmasian, Klini
Augmentasi Citra Pohon Kelapa Sawit untuk Deteksi Objek Berbasis Deep Learning
No full textPenelitian ini menitikberatkan pada Augmentasi citra pohon kelapa sawit untuk deteksi objek menggunakan pendekatan Deep Learning. Pohon kelapa sawit memiliki peran penting dalam industri perkebunan dan pertanian, sehingga pengembangan metode deteksi pohon kelapa sawit yang efisien menjadi krusial dalam pemantauan perkebunan dan pengelolaan sumber daya alam. Metode penelitian melibatkan augmentasi citra, seperti flip, crop, hue, saturation, brightness, exposure dan pra-pemrosesan auto orient dan resize untuk meningkatkan kualitas data pelatihan. Model Deep Learning yang digunakan adalah Convolutional Neural Network (CNN) yang terintegrasi dengan teknik object detection, memungkinkan identifikasi pohon kelapa sawit dari latar belakang dengan akurasi tinggi. Penelitian ini menggunakan 101 citra kepala sawit dan setelah dilakukan augmentasi berjumlah 253 citra pohon kelapa sawit yang bervariasi dalam kondisi pencahayaan, sudut pandang, dan penutupan daun. Hasil eksperimen menunjukkan bahwa metode ini mampu mengidentifikasi pohon kelapa sawit dengan akurasi yang baik, bahkan dalam kondisi yang kompleks. Hasil penelitian ini memiliki potensi aplikasi dalam pemantauan perkebunan kelapa sawit, perencanaan lahan, dan pemantauan lingkungan. Dengan peningkatan akurasi deteksi dan ekstraksi, manajemen perkebunan dan pemantauan lingkungan dapat menjadi lebih efisien dan berkelanjutan
Klasifikasi Sentimen Komentar Pengguna pada Aplikasi Ruangguru Menggunakan Algoritma Naive Bayes
No full textThe advancement of digital technology has encouraged the increasing use of online learning applications such as Ruangguru, while simultaneously fostering various innovations in the field of education. Ruangguru, as one of the most popular educational applications in Indonesia, receives thousands of user comments that can be analyzed to reflect user satisfaction and perception. This study aims to automatically classify user comments based on the sentiments they contain using the Naïve Bayes Classifier algorithm. This approach is expected to help Ruangguru developers better understand user needs and preferences, thereby improving service quality. The dataset was obtained from the Google Play Store platform, consisting of approximately 5,000 comments collected during the period from October 28 to December 31, using the google-play-scraper tool. The application of the Multinomial Naïve Bayes algorithm with TF-IDF weighting was employed to analyze the data, resulting in four sentiment categories: Baik Sekali, Baik, Cukup Baik, and Kurang Baik. Evaluation of the model was conducted using accuracy, precision, recall, and F1-score metrics. With an accuracy rate of 84.83%, the model correctly predicted the actual labels in approximately 85% of the test data. The model also achieved an F1-score of 85%, a precision of 86%, and a recall of 85%. The classification results revealed that the “Baik” category dominated with a proportion of 28.3%, followed by “Baik Sekali” at 24.3%, “Cukup Baik” at 24.0%, and “Kurang Baik” at 23.4%. These findings indicate that the model maintains a reasonable balance between sensitivity and accuracy in sentiment classification. Therefore, the Naïve Bayes Classifier method is capable of automatically identifying user opinions and has the potential to serve as a valuable tool in sentiment analysis for online learning services
Penerapan Metode EUCS dan SMARTPLS Terhadap Pengukuran Tingkat Kepuasan Pengguna Aplikasi Zalora
No full textZalora is an E-Commerce aplplication founded on 2012 in Singapore and now operates in various countries, including Indonesia. Although widely known, this application still has a number of complaints from users such as product incompatibility, delays in process updates, and less than optimal application performance. This study aims to evaluate user satisfaction with the Zalora application using the EUCS method, which includes five variables, namely Content, Accuracy, Format, Ease of Use, and Timeliness. This study provides benefits for application, with the subjects being student college members of the Faculty of Computer Science at Sriwijaya University. This study employs a quantitative approach, utilizing data collection through questionnaires and analysis with SmartPLS software. As a result, the five EUCS method variables positively impact user satisfaction, particularly the Content, Accuracy, Ease of Use and Timeliness variables
Analisis Sentimen Publik Terkait Danantara Menggunakan Algoritma IndoBERT pada Platform Media Sosial
No full textThis study aims to analyze public sentiment toward the Indonesia Investment Authority (Badan Pengelola Investasi – BPI) Danantara using artificial intelligence technology. Data was collected through crawling using an X API token, resulting in 4,269 tweets stored in CSV format, consisting of 15 columns including tweet text and user metadata. The data underwent a pre-processing stage, including text cleaning, case folding, and tokenization, to prepare it for analysis. Manual labeling was conducted to classify sentiment into three categories: positive (32%), negative (45%), and neutral (23%). Due to class imbalance, a data augmentation technique was applied, increasing the total number of records to 23,623. The IndoBERT-base model was employed using a transfer learning approach for three-class sentiment classification. After five training epochs, the model achieved an accuracy of 97.71%. Evaluation results demonstrate high computational efficiency, with the model capable of processing data quickly. This study highlights the importance of applying artificial intelligence technologies, particularly BERT-based language models, in sentiment analysis in the digital era
PROTECTION SYSTEM INI SOLAR PANEL AGAINST INDUCTION MOTOR LOAD
No full textSolar Power Plants (PLTS) have become one of the solutions to meet the need for clean and sustainable energy. However, like other electrical systems, PLTS is also susceptible to disturbances and problems that can interfere with its performance and reliability. This study aims to design and build a voltage and current protection system at the PLTS inverter output to protect the 1-phase induction motor from damage due to unstable voltage and current. The protection system used is Over/Under Voltage Protector, and MCB for overvoltage and undervoltage protection and load current limitation, in order to protect electrical equipment connected to the PLTS system. The test results show that the protection system implemented on the Inverter Output can protect against overvoltage above 250 V, undervoltage below 185 V, and overcurrent.where if this happens then the electricity supply to the load will be automatically cut of
PEMBIMBINGAN OLIMPIADE SAINS NASIONAL BIDANG INFORMATIKA KOMPUTER SMAN 7 BALIKPAPAN
No full textProgram pengabdian di SMAN 7 Balikpapan bertujuan untuk meningkatkan pemahaman siswa dalam algoritma dan pemrograman C++ sebagai persiapan untuk Olimpiade Sains Nasional (OSN) bidang Informatika. Tantangan yang dihadapi meliputi kurangnya pemahaman siswa tentang algoritma dan keterbatasan sumber daya pengajaran. Program ini menggunakan metode pelatihan intensif yang mencakup teori, praktik, dan simulasi soal OSN, disertai evaluasi berkala. Hasilnya menunjukkan peningkatan signifikan dalam pemahaman siswa, dengan skor rata-rata pre-test meningkat dari 60% menjadi 90% setelah pelatihan, menunjukkan kesiapan siswa untuk bersaing di tingkat nasional
PELATIHAN GURU SMK TKJ DALAM MENGGUNAKAN CISCO PACKET TRACER BERBASIS MODEL PROBLEM BASED LEARNING
No full textGuru Teknik Komputer dan Jaringan (TKJ) berperan penting dalam mempersiapkan siswa menghadapi tantangan dan peluang yang dihadirkan dunia digital, terutama kemampuan di bidang teknologi jaringan komputer. Model pembelajaran berbasis masalah (PBL) adalah suatu pendekatan pembelajaran dimana siswa belajar dengan memecahkan masalah-masalah terkait jaringan komputer dan teknologinya, yang dapat meningkatkan pemahamannya terhadap konsep dan keterampilan yang diajarkan. Pelatihan ini dirancang untuk membekali guru TKJ dengan pengetahuan dan keterampilan yang diperlukan untuk mengintegrasikan Cisco Packet Tracer secara efektif ke dalam praktikum dan mempersiapkan mereka untuk menerapkan pendekatan PBL di kelas mereka. Hasil dari pelatihan ini akan meningkatkan mutu pendidikan TKJ di SMK dan menjamin peserta didik memperoleh keterampilan yang diperlukan untuk menghadapi tantangan dan peluang dunia digital serta menjadi tenaga profesional TKJ yang kompeten dan berdaya saing terapan masa depan
Impelementasi Algoritma LSTM Dan SVR Untuk Prediksi Harga Bitcoin Menggunakan Data Yahoo Finance: Indonesia
No full textTechnological developments in the financial sector have facilitated the emergence of various digital investment instruments, one of which is cryptocurrency. Bitcoin and Ethereum are digital assets with the largest market capitalization, while the USD remains a significant player in global trade. The high price volatility of these three assets demands accurate and adaptive prediction methods. This study aims to apply the Long Short-Term Memory (LSTM) learning algorithm to predict Bitcoin, Ethereum, and USD prices based on historical data from Yahoo Finance from 2019 to 2024. Preprocessing includes data normalization with a Min-Max Scaler and feature engineering in the form of daily returns. Model evaluation was conducted using the Mean Absolute Error (MAE) and Mean Squared Error (MSE) metrics. The results showed that the LSTM model performed best, with the lowest MAE value of 1,320.41 and an MSE of 3,464,596.53 for the highest price prediction. These findings demonstrate that LSTM excels in consistently handling complex and fluctuating data patterns. This research is expected to serve as a reference in the development of a machine learning-based digital asset price prediction system, particularly for assets with high volatility
Implementasi YOLOv11 dan Google ML Kit untuk Pembacaan Struk pada Aplikasi Keuangan Mobile
No full textThis study aims to develop an Android-based system capable of automatically recapping shopping data from cashier receipts. The system integrates the YOLOv11 object detection method to identify key information areas such as product names, quantity, unit price, and total amount, and utilizes Google ML Kit as the Optical Character Recognition (OCR) module to extract text from receipt images. The research stages include problem identification, system design, prototype development, and performance evaluation using the Confusion Matrix method. The testing results show a precision of 100%, recall of 74.96%, and an F1-score of 85.7%, indicating that the system performs with high accuracy and effectiveness in extracting receipt information. Therefore, this system offers a practical and efficient solution for automatic expense recording through mobile devices