Abstract:Abstract: This study aims to examine the perceptions of students in the D4 Islamic Finance and Banking Study Program at Politeknik Negeri Medan regarding the implementation of the salam contract (akad salam) in food-ordering…
ring transactions through GoFood using GoPay digital payment, as well as the compliance of such transaction with the principles of fiqh muamalah. This study employes a qualitative method with a descriptive approach. Data were collected through interviews with 11 students selected using purposive sampling. The results indicate that students' perceptions of the implementation of the salam contract (akad salam) in GoFood transactions using GoPay were generally positive. Students understood that the transaction mechanism possesses the characteristics of a salam contract (akad salam) and considered it to be consistent with the principles of fiqh muamalah. Furthermore, GoFood transactions using GoPay were considered to fulfill the requirements of a salam contract (akad salam), including advance payment, clear product specifications, a clearly defined delivery time, mutual agreement between the parties, and delivery of the goods in accordance with the agreed order. Therefore, food ordering transactions through GoFood using GoPay digital payment are considered to comply with the provisions of the salam contract (akad salam) from the perspective of fiqh muamalah.
Keywords: Student Perceptions, Salam Contract, Digital Transactions
Abstrak: Penelitian ini bertujuan untuk mengetahui persepsi mahasiswa Program Studi D4 Keuangan dan Perbankan Syariah Politeknik Negeri Medan terhadap penerapan akad salam pada transaksi pemesanan makanan melalui GoFood dengan pembayaran digital GoPay serta kesesuaiannya dengan fiqh muamalah. Penelitian ini menggunakan metode kualitatif dengan pendekatan deskriptif. Data diperoleh melalui wawancara terhadap 11 mahasiswa yang dipilih menggunakan teknik purposive sampling. Hasil penelitian menunjukkan bahwa persepsi mahasiswa terhadap penerapan akad salam pada transaksi GoFood dengan pembayaran GoPay cenderung positif. Mahasiswa memahami bahwa mekanisme transaksi tersebut memiliki karakteristik akad salam dan menilai telah sesuai dengan prinsip fiqh muamalah. Selain itu, transaksi GoFood dengan pembayaran GoPay dinilai telah memenuhi ketentuan akad salam, yaitu pembayaran di muka, kejelasan spesifikasi barang, kejelasan waktu penyerahan, kesepakatan para pihak, serta penyerahan barang sesuai pesanan. Dengan demikian, transaksi pemesanan makanan melalui GoFood dengan pembayaran digital GoPay telah sesuai dengan ketentuan akad salam dalam perspektif fiqh muamalah.
Kata kunci: Persepsi Mahasiswa, Akad Salam, Transaksi Digital
Abstract:Abstract: Selecting appropriate vocational training fields for prospective workers at Job Training Centers (BLK) is critical for optimizing skill development and ensuring job market alignment. However, manual selection processes…
rocesses and high-dimensional datasets containing irrelevant candidate attributes often lead to classification inaccuracies and computational noise. This study proposes an optimized classification model combining the Information Gain (IG) feature selection method with the Random Forest algorithm to enhance prediction accuracy for training field suitability. Using a dataset of 233 candidate records, feature weighting was executed via Information Gain to eliminate redundant demographic attributes. The feature selection identified four dominant predictors: Academic Potential Test (TPA) score, interview score, vocational skill score, and BLK training choice. Experimental evaluation was conducted using a 70:30 train-test split in RapidMiner Studio. The experimental results demonstrate that applying Information Gain reduced the feature space from nine to four attributes, increasing classification accuracy from 87.14% (baseline Random Forest) to 90.00% (+2.86%). Furthermore, the model achieved a notable precision enhancement, rising from 86.96% to 93.44% (+6.48%), and an improved F1-Score of 94.21%. These findings confirm that removing non-contributing demographic variables mitigates dataset noise and enables Random Forest to form more effective decision boundaries. The optimized model offers a robust, objective framework to support decision-making systems in candidate placement at vocational training institutions.
Keywords: Information Gain, Random Forest, Feature Selection, Training Field Classification, RapidMiner.
Abstrak: Penentuan bidang pelatihan vokasi yang tepat bagi calon peserta di Balai Latihan Kerja (BLK) sangat krusial untuk mengoptimalkan pengembangan keterampilan dan kesesuaian dengan kebutuhan pasar kerja. Namun, proses seleksi manual dan tingginya dimensi dataset yang memuat atribut kandidat kurang relevan sering kali menyebabkan ketidakakuratan klasifikasi serta noise komputasi. Penelitian ini mengusulkan model klasifikasi teroptimasi yang menggabungkan metode seleksi fitur Information Gain (IG) dengan algoritma Random Forest untuk meningkatkan akurasi prediksi kesesuaian bidang pelatihan. Menggunakan dataset sebanyak 233 data calon peserta, pembobotan fitur dieksekusi melalui Information Gain untuk mengeliminasi atribut demografis yang redundan. Hasil seleksi fitur berhasil mengidentifikasi empat prediktor dominan: nilai Tes Potensi Akademik (TPA), nilai wawancara, nilai kejuruan, dan pilihan BLK. Evaluasi eksperimental dilakukan dengan pembagian data train-test sebesar 70:30 pada perangkat lunak RapidMiner Studio. Hasil pengujian membuktikan bahwa penerapan Information Gain yang mereduksi ruang fitur dari sembilan menjadi empat atribut mampu meningkatkan akurasi klasifikasi dari 87,14% (model awal Random Forest) menjadi 90,00% (+2,86%). Selain itu, model ini mencapai peningkatan presisi yang signifikan, naik dari 86,96% menjadi 93,44% (+6,48%), serta kenaikan F1-Score menjadi 94,21%. Temuan ini mengonfirmasi bahwa penghapusan variabel demografis yang tidak berkontribusi berhasil mengurangi noise data dan memungkinkan Random Forest membentuk pohon keputusan yang lebih efektif. Model teroptimasi ini memberikan kerangka kerja yang objektif dan andal untuk mendukung sistem pengambilan keputusan dalam penempatan calon peserta di lembaga pelatihan vokasi.
Kata kunci: Information Gain, Random Forest, Seleksi Fitur, Klasifikasi Bidang Pelatihan, RapidMiner.
Abstract:Abstract: The equitable availability of health workers is an essential factor in improving the quality of healthcare services. The unequal distribution of nurses across regencies and cities in North Sumatra Province has…
created the need for an approach capable of identifying the level of health workforce availability quickly and objectively. This study aims to implement the Single Layer Perceptron model to classify the level of health workforce availability based on the number of nurses, population, and public health centers. The study utilized secondary data obtained from the North Sumatra Provincial Health Office for the period of 2019–2024. Prior to the training process, the data were normalized using the Min-Max Normalization method and divided into 80% training data and 20% testing data. The training process employed a learning rate of 0.1, a maximum of 3,000 epochs, and an error tolerance of 0.01. The proposed model classified the data into two categories, namely low and high, and was implemented in a web-based application that supports data training, prediction, and classification result evaluation. The results demonstrate that the Single Layer Perceptron model is capable of recognizing patterns in health workforce availability and performing classification effectively, thereby supporting decision-making processes related to the equitable distribution of health workers in North Sumatra Province. Furthermore, the developed system facilitates data management and provides computerized classification results efficiently.
Keywords: Single Layer Perceptron, Classification, Artificial Neural Network, Health Workforce, North Sumatra Province.
Abstrak: Ketersediaan tenaga kesehatan yang merata merupakan faktor penting dalam meningkatkan kualitas pelayanan kesehatan. Ketimpangan distribusi tenaga perawat antar kabupaten/kota di Provinsi Sumatera Utara menyebabkan perlunya suatu pendekatan yang mampu mengidentifikasi tingkat ketersediaan tenaga kesehatan secara cepat dan objektif. Penelitian ini bertujuan mengimplementasikan model Single Layer Perceptron untuk mengklasifikasikan tingkat ketersediaan tenaga kesehatan berdasarkan data jumlah perawat, jumlah penduduk, dan jumlah puskesmas. Data yang digunakan merupakan data sekunder dari Dinas Kesehatan Provinsi Sumatera Utara periode 2019–2024. Sebelum proses pelatihan, data dinormalisasi menggunakan metode Min-Max Normalization, kemudian dibagi menjadi 80% data latih dan 20% data uji. Proses pelatihan menggunakan learning rate sebesar 0,1, maksimum epoch sebanyak 3000, serta error tolerance sebesar 0,01. Model menghasilkan klasifikasi ke dalam dua kategori, yaitu rendah dan tinggi, serta diimplementasikan dalam aplikasi berbasis web yang mendukung proses pelatihan, prediksi, dan evaluasi hasil klasifikasi. Hasil penelitian menunjukkan bahwa Single Layer Perceptron mampu mengenali pola ketersediaan tenaga kesehatan dan memberikan hasil klasifikasi secara efektif sehingga dapat mendukung proses pengambilan keputusan dalam pemerataan tenaga kesehatan di Provinsi Sumatera Utara. Implementasi sistem yang dikembangkan juga mempermudah pengelolaan data dan penyajian informasi klasifikasi secara terkomputerisasi.
Kata Kunci: Single Layer Perceptron, klasifikasi, jaringan saraf tiruan, tenaga kesehatan, Provinsi Sumatera Utara.
Abstract:Rainfall is one of the most important climatological parameters that plays a significant role in supporting agriculture, water resource management, and hydrometeorological disaster mitigation in Indonesia. As the volume…
of meteorological data continues to increase, technologies capable of processing large-scale data efficiently are required. This study aims to analyze rainfall patterns in Indonesia using a Big Data approach by utilizing Apache Spark as the primary data processing framework. The data used in this research consist of daily rainfall data from 34 provinces in Indonesia obtained from the Meteorology, Climatology, and Geophysics Agency (BMKG) in CSV format. This research employs a descriptive quantitative method consisting of data ingestion, data cleaning, data transformation, and descriptive statistical analysis using Apache Spark with PySpark. The analysis was conducted to obtain information regarding the number of valid records, average rainfall, maximum rainfall, and minimum rainfall values for each province. The results indicate that Apache Spark is capable of processing rainfall data in a structured, efficient, and automated manner. Based on the analysis, West Sumatra Province recorded the highest average rainfall at 16.01 mm/day, while Central Sulawesi Province recorded the lowest average rainfall at 0.30 mm/day. In addition, the highest maximum rainfall was observed in Bengkulu Province at 186.4 mm. The use of Spark DataFrame and aggregation functions such as count(), avg(), max(), and min() proved effective in supporting data processing and analysis activities. The findings demonstrate that Apache Spark can serve as an effective solution for large-scale climatological data processing and provide valuable information to support decision-making in agriculture, water resource management, and disaster mitigation in Indonesia.
Abstract:Public services at the village level are still largely delivered conventionally, causing limitations in service speed, information transparency, and data updating. This Community Service (KKN) activity aims to digitize public…
ublic services in Serdang Village, Meranti Sub-district, Asahan Regency, by developing a web-based Village Information Portal accessible at desa-serdang.my.id. The portal was built using the open-source OpenSID platform, which was customized and populated with village data to fit local needs. The implementation followed common development stages: requirements analysis through observation and interviews, installation and configuration on hosting and a domain, interface customization and content entry, module activation, testing, and training for officials. The portal provides village profiles and officials, a GIS-based map, a photo gallery, news, transparency documents such as the village budget (APBDes) and LPPD, population statistics, an MSME catalog and citizen service board, and a Citizen Self-Service feature for online letter requests, social-aid verification, and complaints. Black-box testing showed all features worked as intended. The portal eases public access to information and services, strengthens governance transparency, and opens space to promote the local economy, becoming an initial step in the digital transformation of Serdang Village toward a smart village.
Abstract:Abstract: Since December 2019, the COVID-19 pandemic has spread worldwide, posing a grave danger to global public health and severely threatening healthcare systems. Therefore, early prediction of COVID-19 case severity…
is crucial for saving lives and optimizing healthcare logistics. In this study, we utilize a blood sample dataset from infected patients in Wuhan, China, and construct machine learning models to predict patient mortality risk. Given that the testing data includes only three biomarkers, the experiments are divided into two parts: an "all features" scenario and a "three features" scenario. Based on feature selection and model optimization, candidate models are configured with different feature subsets and evaluated using both single algorithms and ensemble models on the training data. The trained models are then evaluated on the testing data. The results demonstrate that ensemble models achieve superior performance compared to single models across both feature scenarios. Notably, ensemble models using the "three features" configuration achieve the highest overall performance among all experimental setups, despite relying on fewer features. Our top F1 score for the positive class (death) approaches the baseline results, confirming the effectiveness of our experimental framework.
Keywords: Predictive model, COVID-19 mortality, data mining, feature selection
Abstrak: Sejak Desember 2019, wabah COVID-19 telah menyebar ke seluruh dunia, tidak hanya membahayakan nyawa dan kesehatan penduduk global serta mengancam sistem layanan kesehatan masyarakat secara serius. Oleh karena itu, prediksi dini mengenai tingkat keparahan kasus COVID-19 sangatlah penting untuk kehidupan masyarakat dan perencanaan logistik kesehatan. Dalam penelitian ini, kami memanfaatkan basis data sampel darah dari pasien yang terinfeksi di wilayah Wuhan, Tiongkok, dan membangun model pembelajaran mesin (machine learning) untuk memprediksi risiko kematian pasien tersebut. Mengingat data pengujian hanya mencakup tiga biomarker, eksperimen ini dibagi menjadi dua bagian: skenario "semua fitur" dan "tiga fitur". Berdasarkan pemilihan fitur dan model, berbagai model dikonfigurasi dengan rangkaian fitur yang berbeda, lalu menerapkan model tunggal maupun model ensemble pada data pelatihan. Selanjutnya, model yang telah dilatih tersebut diuji menggunakan data pengujian. Hasil penelitian menunjukkan bahwa model ensemble mencapai kinerja yang lebih baik dibandingkan model tunggal, baik pada skenario "semua fitur" maupun "tiga fitur". Hasil dari model ensemble pada skenario "tiga fitur" merupakan yang tertinggi di antara seluruh rangkaian eksperimen, meskipun menggunakan jumlah fitur yang lebih sedikit. Skor F1 tertinggi kami untuk sampel positif (death) mendekati hasil eksperimen dasar (baseline), yang menunjukkan efektivitas pendekatan eksperimental dalam penelitian ini.
Kata kunci: Model prediktif, mortalitas COVID-19, penambangan data, pemilihan fitur
Abstract:Latar Belakang: Waktu tunggu merupakan salah satu indikator Standar Pelayanan Minimal (SPM) Rumah Sakit sekaligus komponen penting yang memengaruhi kepuasan pasien rawat jalan. Rumah Sakit Ibu dan Anak (RSIA) Aulia Jakarta…
ta menghadapi keluhan pasien terkait lamanya waktu tunggu serta indikasi pasien batal berobat, sehingga diperlukan gambaran awal mengenai kondisi waktu tunggu pelayanan rawat jalan di rumah sakit tersebut. Tujuan: Memperoleh gambaran awal mengenai waktu tunggu pelayanan rawat jalan serta kesesuaiannya dengan SPM Rumah Sakit di RSIA Aulia Jakarta. Metode: Penelitian ini merupakan studi pendahuluan (preliminary study) dengan desain deskriptif, memanfaatkan data survei kepuasan pasien rawat jalan (Desember 2013, n=230), data rekapitulasi komplain pasien tahun 2013, dan observasi langsung terhadap waktu tunggu pasien pada hari sibuk (peak hour), yaitu Sabtu, 1 Maret 2014 (n=128), di enam poliklinik RSIA Aulia. Hasil: Survei pendahuluan menunjukkan 23% pasien menilai pelayanan rawat jalan baik, 67% cukup, dan 7% kurang baik. Pada hari observasi, 4,8% pasien (7 dari 135 pasien yang mendaftar) batal berobat. Total waktu tunggu pelayanan rawat jalan di Poliklinik Anak dan Poliklinik Kandungan sebagian besar melebihi SPM (≤ 60 menit), yaitu 90% pasien Poli Anak dan 53,12% pasien Poli Kandungan menunggu lebih dari 60 menit, sedangkan Poliklinik Penyakit Dalam, Gigi, Umum, dan KIA sebagian besar telah memenuhi standar tersebut. Keterlambatan praktik dokter merupakan keluhan pasien yang paling banyak tercatat sepanjang tahun 2013. Kesimpulan: Waktu tunggu pelayanan rawat jalan di RSIA Aulia, khususnya di Poliklinik Anak dan Kandungan yang memiliki volume kunjungan tertinggi, belum sepenuhnya memenuhi SPM Rumah Sakit. Diperlukan penelitian lanjutan yang lebih mendalam untuk menggali faktor input dan proses pelayanan yang memengaruhi kondisi tersebut.
Abstract:Abstract: This study involved the design and construction of an Internet of Things (IoT)-based Smart Buoy prototype to serve as an early warning system for extreme weather in the waters of Dumai City. The research employed…
ed a Research and Development (R&D) approach, encompassing hardware and software design, system integration, and performance testing. Weather data—including air temperature, atmospheric pressure, rainfall, and wind speed—are collected via sensors, processed by a microcontroller, and transmitted in real-time over the internet to a monitoring platform. The study yielded the following results: (1) the Smart Buoy prototype is capable of measuring and monitoring weather parameters in real-time; (2) the IoT system successfully transmits measurement data to the monitoring platform, enabling remote weather monitoring; and (3) the integration of air temperature, atmospheric pressure, rainfall, and wind speed data serves as a basis for identifying potentially extreme weather conditions and issuing early warnings to users. Consequently, this IoT-based Smart Buoy prototype offers an effective alternative for weather monitoring and extreme weather early warning, thereby supporting the safety of fishermen and maritime transport users in the waters of Dumai City.
Keywords: Smart Buoy, Internet of Things (IoT), Extreme Weather Monitoring
Abstrak: Penelitian ini merancang dan membangun prototipe Smart Buoy berbasis Internet of Things (IoT) sebagai sistem peringatan dini cuaca ekstrem di perairan Kota Dumai. Penelitian menggunakan metode metode penelitian menggunakan pendekatan research and development (R&D) dengan tahapan perancangan perangkat keras, perangkat lunak, integrasi sistem, serta pengujian kinerja alat. Data kondisi cuaca diperoleh melalui sensor yang mengukur suhu udara, tekanan atmosfer, curah hujan, dan kecepatan angin, kemudian diproses oleh mikrokontroler dan dikirimkan secara real-time melalui jaringan internet ke platform pemantauan. Hasil penelitian bertujuan yaitu: (1) prototipe Smart Buoy mampu melakukan pengukuran dan pemantauan parameter cuaca secara real-time; (2) sistem IoT mampu mengirimkan data hasil pengukuran ke platform pemantauan sehingga kondisi cuaca dapat dipantau dari jarak jauh; dan (3) integrasi parameter suhu udara, tekanan atmosfer, curah hujan, dan kecepatan angin dapat digunakan sebagai dasar dalam menentukan kondisi cuaca yang berpotensi ekstrem serta memberikan peringatan dini kepada pengguna. Dengan demikian, prototipe Smart Buoy berbasis IoT dapat menjadi salah satu alternatif sistem pemantauan dan peringatan dini cuaca ekstrem yang efektif dalam mendukung keselamatan nelayan dan pengguna transportasi laut di perairan Kota Dumai.
Kata kunci: Smart_Buoy, Internet_of_Things (IoT), Pemantauan_Cuaca_Ekstrem
Abstract:Abstract: This study aims to analyze the influence of human resource competence and the utilization of information technology on the effectiveness of regional financial management at the Regional Revenue Agency of Karo Regency.…
egency. The independent variables examined are human resource competence and information technology utilization, while the dependent variable is the effectiveness of regional financial management. A quantitative research method was employed, involving primary data collection through questionnaires distributed to employees involved in financial management. Statistical tests were used to analyze the data and examine the partial and simultaneous effects of the independent variables on the dependent variable. The results indicate that human resource competence and information technology utilization each have a positive and significant partial effect on the effectiveness of regional financial management. Simultaneously, both variables also exert a significant influence, with an R-square value of 75.7%, demonstrating that these two factors account for a substantial portion of the variation in financial management effectiveness. The study recommends enhancing employee competence through training and optimizing the use of information technology to improve the effectiveness of regional financial management in Karo Regency.
Keywords: Human Resource Competence, Information Technology Utilization, Effectiveness of Regional Financial Management
Abstrak: Penelitian ini bertujuan untuk menganalisis pengaruh kompetensi sumber daya manusia dan pemanfaatan teknologi informasi terhadap efektivitas pengelolaan keuangan daerah di Badan Pendapatan Daerah Kabupaten Karo. Variabel independen yang diteliti adalah kompetensi sumber daya manusia dan pemanfaatan teknologi informasi, sedangkan variabel dependen adalah efektivitas pengelolaan keuangan daerah. Metode penelitian yang digunakan adalah kuantitatif dengan pengumpulan data primer melalui penyebaran kuesioner kepada pegawai yang terlibat dalam pengelolaan keuangan. Analisis data menggunakan uji statistik untuk menguji pengaruh parsial dan simultan dari variabel bebas terhadap variabel terikat. Hasil penelitian menunjukkan bahwa kompetensi sumber daya manusia dan pemanfaatan teknologi informasi secara parsial berpengaruh positif dan signifikan terhadap efektivitas pengelolaan keuangan daerah. Secara simultan, kedua variabel tersebut juga berpengaruh secara signifikan, dengan nilai R Square sebesar 75,7%, yang menunjukkan bahwa kedua faktor ini mampu menjelaskan sebagian besar variasi efektivitas pengelolaan keuangan. Penelitian ini merekomendasikan peningkatan kompetensi pegawai melalui pelatihan dan optimalisasi penggunaan teknologi informasi guna meningkatkan efektivitas pengelolaan keuangan daerah di Kabupaten Karo.
Kata kunci: Kompetensi Sumber Daya Manusia, Pemanfaatan Teknologi Informasi, Efektivitas Pengelolaan Keuangan Daerah.
Abstract:This study aims to analyze the effectiveness level of the Lembang Income and Expenditure Budget (APBL) utilization in Lembang Baruppu’, Benteng Batu District, North Toraja Regency, for the 2021–2024 period, and to identify…
entify the inhibiting factors that affect budget absorption. The research method employed is descriptive quantitative. The data used consists of secondary data sourced from the Realization Reports of the Income and Expenditure Budget of Lembang Baruppu’ from 2021 to 2024. The data analysis technique was carried out by calculating the financial effectiveness ratio based on the comparison between the budget realization and the predetermined targets, referring to the standards of Kepmendagri No. 600.900-327 of 1996. The results of the study indicate that the revenue effectiveness level in Lembang Baruppu’ for the 2021–2024 period falls into the highly effective and effective categories, with the average percentage of revenue realization closely reaching the budget targets each year. However, budget expenditure realization shows a fluctuating and declining trend until 2024. There is a budget variance in the form of Unspent Village Funds (SiLPA) caused by suboptimal expenditure absorption. The primary inhibiting factors in budget absorption include the limited competence and human resource quality of lembang officials in financial administration, technical constraints in adjusting work programs, and geographical challenges in executing physical development activities in the field.