1,720,967 research outputs found
Pengaruh Game Online terhadap Akhlak Siswa di Mts Darul Amanah Kecamatan Bati-bati Kabupaten Tanah Laut
Get PDFPenelitian ini bertujuan untuk mengetahui intensitas siswa dalam penggunaan game online dan untuk mengetahui akhlak siswa di mts darul amanah yang menggunakan game online.
Populasi dalam penelitian ini adalah siswa kelas VIII dan IX di Mts Darul Amanah yang berjumlah 96 orang siswa dengan rincian 25 siswa kelas VIII A, 24 siswa kelas VIII B, 23 siswa kelas IX A dan 24 siswa kelas IX B. Teknik pengambilan sampel dalam penelitian ini adalah total sampling yaitu dimana jumlah sampel sama dengan jumlah populasi, karena sampel kurang dari 100.
Penelitian ini menggunakan jenis penelitian lapangan (field Research) dengan pendekatan kuantitatif. Metode yang digunakan adalah penelitian deskriptif. Teknik pengumpulan data dalam penelitian ini menggunakan angket, observasi dan dokumentasi. Teknik pengujian data terdiri dari uji validitas dan uji reliabilitasnya yang banyak digunakan pada penilitian yaitu menggunakan metode Cronbach Alpha, kriteria yang dipersyaratkan adalah sama atau lebih besar dari 0,60 dengan bantuan program SPSS 22.0. diketahui pada penelitian ini menunjukkan reliabilitas X adalah 0,854 dan Y adalah 0,617. Hasil analisis data yang telah dilakukan terhadap penelitian ini menunjukkan bahwa terdapat pengaruh antara game online secara negatif terhadap akhlak siswa kelas VIII dan IX di MTS Darul Amanah dimana semakin tinggi intesitas bermain game online semakin rendah akhlak siswa. Saran bagi peneliti selanjutnya yaitu dapat melakukan penelitian lanjutan di wilayah dan di tempat yang sama atau di wilayah lain. Peneliti dapat menggunakan variabel lain yang mengarah kepada faktor yang mempengaruhi munculnya perilaku akhlak yang lainnya
From leaf to cup: critical points of halal compliance in artisan tea
Get PDFIndonesian tea consumption has posted a consistent upward trend, both in volume and market demand for a greater variety of tea products. Among the popular new varieties is artisan tea, which is a creatively crafted blend of tea leaves mixed with natural ingredients such as spices, flowers, and dried fruits to produce distinctive tastes and a good appearance. Although the ingredients used are plant-based and generally halal, the artisan tea manufacturing process can still include critical control points that require rigorous halal testing. The goal of this study is to identify and assess the halal critical points of artisan tea manufacturing according to three aspects: raw materials, manufacturing process, and packaging materials. A qualitative descriptive research design was utilized, including literature review, direct observation of the manufacturing process, and analysis using the halal critical point decision tree framework. It discovers that possible points of halal criticality at the artisan stage of tea-making pertain almost solely to packing material—essentially those bearing plastic-based or synthetically embedded material, those occasions of mixing handled manually as perhaps a hazard against contamination, and storage needs such that protective containment from access of uncleaned substances or cross-contamination should be required. In contrast, ingredients such as green tea, dried mint leaves, and Indian cardamom, being the major ingredients, when used in their dry forms, are usually exempted from having severe halal concerns on account of their plant origin and minimal processing and are thus inherently halal.
Keywords: Tea, Artisan Tea, Halal Critical Points, Hala
Model Prognosis Masa Pengobatan Pasien Tuberkulosis Dengan Metode C4.5
Get PDFPasien Tuberkulosis mempunyai jangka waktu pengobatan yang relatif beragam karena tingkat kepatuhan tiap pasien untuk meminum obat sampai dengan habis dan jangka waktu yang sudah ditentukan oleh Dokter Spesialis Paru. Apabila salah diagnosa terkait dosis obat maka akan meningkatkan faktor resiko kesehatan yaitu dimana proses pengobatan akan lebih memakan waktu dan lebih lama karena adanya kondisi Multi-Drug Resistant. Hal ini yang harus menjadi perhatian semua pihak agat tingkat kegagalan atas proses pengobatan pasien Tuberkulosis harus ditekan se minimal mungkin. Faktor kebiasaan pasien dan waktu minum obat pasien harus dijaga ketat agar masa pengobatan dapat lebih dipersingkat. Dokter Spesialis Paru berupaya untuk menekan tingkat Drop Out pasien Tuberkulosis dengan cara mengawasi jadwal mereka dengan pengelolaan yang baik. Oleh karena itu, dibutuhkan sistem untuk membantu proses prediksi masa pengobatan pasien dengan menerapkan Cross-Industry Standard Process for Data Mining (CRISP-DM) dan menggunakan pendekatan data mining dengan mengimplementasikan algoritma C4.5 setelah dilakukan eksplorasi data menggunakan beberapa algoritma untuk klasifikasi dengan tujuan untuk hasil akurasi performa model untuk prognosis masa pengobatan pasien tuberkulosis. Melalui tahap Data Understanding dan Data Preprocessing menghasilkan atribut baru yaitu Lama Pengobatan. Dengan menggunakan 596 record mendapatkan hasil akurasi sebesar 74.33%.
Abstract
Tuberculosis patients have a relatively diverse treatment period because of the level of compliance of each patient to take the drug until it runs out and the time period has been determined by the Pulmonary Specialist. If a wrong diagnosis is related to drug dosage, it will increase health risk factors, namely where the treatment process will take more time and longer due to the Multi-Drug Resistant condition. This should be the concern of all parties so that the failure rate of the treatment process for tuberculosis patients must be kept to a minimum. The patient\u27s habit factor and the patient\u27s time to take medication must be closely monitored so that the treatment period can be shortened. Pulmonary Specialists try to reduce the Drop Out rate of Tuberculosis patients by monitoring their schedule with good management. Therefore, a system is needed to help predict the patient\u27s treatment period by applying the Cross-Industry Standard Process for Data Mining (CRISP-DM) and using a data mining approach by implementing the C4.5 algorithm after exploring the data using several algorithms for classification with the aim of for the results of model performance accuracy for the prognosis of the treatment period of tuberculosis patients. Through the Data Understanding and Data Preprocessing stages, a new attribute is produced, namely the Length of Treatment. By using 596 records to get an accuracy of 74.33%
PENERAPAN ALGORITME BACKPROPAGATION NEURAL NETWORK UNTUK ESTIMASI JUMLAH KASUS DBD BERDASARKAN DATA CUACA
No full textDengue fever is widespread throughout the tropics which tends to have a seasonal pattern, namely before and after the rainy season. Infection is caused by one of the closely related dengue viruses, commonly called a serotype, which causes mild symptoms to symptoms that require medical treatment and hospitalization, even death can occur if the case is severe. Based on surveillance data, the number of cases in 2022 will be 3,190 people. One of the efforts to reduce the incidence of DHF is by forecasting the incidence of DHF to prevent an increase in DHF cases which continues every year. This research was forecasted using the independent variables average temperature, average humidity, average rainfall, and wind speed. The data used is public through surveillance and the BMKG website and the data used is data from 2018 to 2022. In this study using the backpropagation neural network algorithm, the model used is 4-3-1, where there are 4 variables in the input layer, 3 units in the hidden layer, 1 unit in the output layer with a learning rate value of 0.04, and momentum of 0.09 and the results are RMSE 4,347
Enhancing Prediction of Treatment Duration in New Tuberculosis Cases: A Comprehensive Approach with Ensemble Methods and Medication Adherence
Get PDFTuberculosis (TB) remains a significant global health problem, with treatment duration varying among patients. TB patients have difficulty following a long-term treatment regimen. After the final diagnosis is determined, it is necessary to know the predicted duration of treatment for a patient. By increasing patient compliance with taking medication, the percentage of TB patients will increase, and this can reduce cases of multi-drug resistant patients and dropouts. This study aims to build a prediction model for the duration of treatment for new cases of Pulmonary TB patients by adding medication compliance parameters using the ensemble method. The research methodology uses CRISP-DM. This study begins with identifying problems and objectives, collecting data, preprocessing and analyzing data, modeling, evaluating, and validating models. The results showed that adding medication compliance parameters can improve model performance. However, the results of model exploration with feature selection techniques and various ensemble methods have not shown good performance. The medication adherence parameters used in this study are the number of medications swallowed in Phase I and Anti-Tuberculosis drug compliance in Phase I. These parameters had never been used in previous studies. The prediction model can be used as an early warning for a patient. If a patient is predicted to have a treatment duration of more than six months, then the patient will receive stricter drug intake supervision. Thus, this proposed model is expected to help achieve the target of eliminating Tuberculosis in 2030 to reduce the death rate by 90% compared to 2019
A Forecasting Modeling of Imported Goods Release Waiting Time in Importer Logistics Operations Using Multiple Linear Regression
No full textImport activities play a critical role in international trade, directly affecting logistics efficiency and the competitiveness of importing companies. The process of releasing imported goods at ports often involves complex administrative procedures that can cause delays, leading to increased logistics costs. This study aims to predict the waiting time for the release of imported goods using a machine learning approach. A case study was conducted at PT. Sentra Sarana Logistic, a licensed customs broker responsible for import administration. The primary model applied was Multiple Linear Regression (MLR), and its performance was compared with Neural Network (NN) and Support Vector Machine (SVM) algorithms. Several influencing factors were considered, including tax payment time, inspection duration, and inspection status. Evaluation results indicate that the MLR model achieved the best performance, with an RMSE of 0.00653, MAE of 0.00544, and R-squared of 0.99999, demonstrating high prediction accuracy and a strong linear correlation. The SVM model yielded acceptable results (RMSE 0.74107, R-squared 0.98388) but underperformed compared to MLR. The NN model showed the lowest accuracy with RMSE 2.86599, MAE 2.38831, and R-squared 0.69510. The findings suggest that MLR, despite its simplicity, is highly effective for predicting waiting times in import logistics operations. This research not only offers a practical decision-support tool for importers but also contributes to the existing literature on machine learning applications in logistics operations and customs processing.Import activities play a critical role in international trade, directly affecting logistics efficiency and the competitiveness of importing companies. The process of releasing imported goods at ports often involves complex administrative procedures that can cause delays, leading to increased logistics costs. This study aims to predict the waiting time for the release of imported goods using a machine learning approach. A case study was conducted at PT. Sentra Sarana Logistic, a licensed customs broker responsible for import administration. The primary model applied was Multiple Linear Regression (MLR), and its performance was compared with Neural Network (NN) and Support Vector Machine (SVM) algorithms. Several influencing factors were considered, including tax payment time, inspection duration, and inspection status. Evaluation results indicate that the MLR model achieved the best performance, with an RMSE of 0.00653, MAE of 0.00544, and R-squared of 0.99999, demonstrating high prediction accuracy and a strong linear correlation. The SVM model yielded acceptable results (RMSE 0.74107, R-squared 0.98388) but underperformed compared to MLR. The NN model showed the lowest accuracy with RMSE 2.86599, MAE 2.38831, and R-squared 0.69510. The findings suggest that MLR, despite its simplicity, is highly effective for predicting waiting times in import logistics operations. This research not only offers a practical decision-support tool for importers but also contributes to the existing literature on machine learning applications in logistics operations and customs processing
PENERAPAN TEKNIK TOKEN ECONOMY UNTUK MENINGKATKAN PERILAKU DISIPLIN BELAJAR PADA PESERTA DIDIK KELOMPOK A TK ISLAM BAKTI IX KERTEN SURAKARTA TAHUN AJARAN 2014
Get PDFPenelitian ini bertujuan untuk meningkatkan perilaku disiplin belajar pesertadidik melalui penerapan teknik token economy pada peserta didik kelompok A TK Islam BaktiIX Kerten Surakarta tahun ajaran 2014. Subjek dalam penelitian ini berjumlah 18 pesertadidik. Penelitian tindakan kelas ini dilaksanakan dalam dua siklus dan setiap siklusmerupakan perbaikan yang didasarkan atas hasil dari refleksi siklus sebelumnya. Setiapsiklus terdiri dari tahap perencanaan, pelaksanaan, pengamatan, dan refleksi. Hasilpenelitian menunjukkan adanya peningkatan perilaku disiplin belajar setelah diterapkanteknik token economy pada peserta didik kelompok A TK Islam Bakti IX Kerten SurakartaTahun Ajaran 2014.Keywords : perilaku, disiplin belajar, token econom
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Tingkat Kelarutan Peptida Tempe dengan Bobot Molekul Kecil pada Berbagai Jenis Pelarut
Get PDFThere are various methods exist to extract soluble peptide from soybean and its fermented products. This study was aimed to evaluate the solubility of low molecular weight peptide of tempe from two types of bean (GMO and non-GMO soybean) and two different treatment (boiling and non-boiling). The solvents used were water and organic solvents which commonly used as solvents for soy-fermented product. The result showed that acetonitrile (A): water (W): trifluoroacetic acid (TF) provided higher solubility of the peptides compared with water (p < 0.05). The addition of trifluoroacetic acid in acetonitrile-water mixture (A1W1) increased the peptide recovery about 1.522 mM (31.7%). The GMO tempe showed the higher content of peptide recovery compared with non-GMO tempe, while boiled tempe also gave higher peptide recovery than non-boiled tempe.
ABSTRAK
Metode ekstraksi peptida terlarut pada produk kedelai dan fermentasi kedelai sangat bervariasi. Penelitian ini dilakukan untuk menganalisa sifat kelarutan peptida dengan berat molekul kecil pada sampel tempe yang diambil dari dua jenis kedelai (GMO dan non-GMO) serta dua jenis perlakuan (perebusan dan tanpa perebusan) yang berbeda. Pelarut yang digunakan meliputi air dan pelarut organik yang umum digunakan dalam ekstraksi peptida kedelai dan produk fermentasinya. Hasil penelitian menunjukkan bahwa pelarut organik asetonitril: air: asam trifluoroasetat (A1W1TF) memberikan tingkat kelarutan peptida tempe kedelai lebih baik dibanding pelarut air (p < 0,05). Penambahan asam trifluoroasetat pada pelarut campuran asetonitril-air (A1W1) terbukti meningkatkan peptida terlarut hingga 1,522 mM (31,7%). Tempe GMO menunjukkan kelarutan peptida lebih tinggi dibanding non-GMO sedangkan proses perebusan juga diketahui mempunyai tingkat kelarutan yang lebih tinggi dibanding tempe tanpa perebusan.
Kata kunci: Asetonitrile; kelarutan; peptida; tempe; asam trifluoroaseta
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