Metode Simple Additive Weighting untuk Pemilihan Website dengan Keaktifan Terbaik (Studi Kasus Website Pemerintah Kota Semarang)

Metode Simple Additive Weighting meyakini bahwa pemilihan alternatif dengan nilai tertinggi dari beberapa pilihan yang ada merupakan teknik pengambilan keputusan multi-kritteria yang sederhana dan efektif. Hal ini dikarenakan dalam pemilihannya disertai dengan evaluasi serta pembandingan alternatif lain berdasarkan sejumlah kriteria yang telah ditentukan sebelumnya.. Penelitian ini bertujuan untuk membandingkan website yang terdaftar dalam Pemerintah Kota Semarang dalam mencari website dengan keaktifan terbaik pada Kecamatan Banyumanik melalui metode Simple Additivve Weighting. Hasilnya menunjukkan bahwa website milik Kelurahan Jabungan memiliki tingkat keaktifan terbaik dibandingkan website lain, di mana pada setiap alternatifnya memiliki nilai tertinggi setelah dilakukannya normalisasi data serta perhitungan bobot kriteria pada masing – masing nilai kriteria

Perbandingan Kinerja Akurasi Model Mesin Learning Untuk Prediksi Penyakit Jantung

This research aims to comprehensively analyze heart disease-related data through Exploratory Data Analysis (EDA), identification of correlations between numerical variables, and cluster analysis to uncover patterns in the data. Furthermore, using various machine learning algorithms, such as Logistic Regression, Support Vector Classifier, Decision Tree Classifier, Random Forest Classifier, K-Nearest Neighbors, and Gaussian Naive Bayes, a heart disease prediction model was built. The model evaluation shows that Naive Bayes has the highest test accuracy of 90%, followed by RandomForestClassifier and KNeighborsClassifier which have 85% test accuracy. These findings indicate a good ability to predict heart disease, but further analysis is needed to ensure good generalization to unseen data. This research makes an important contribution to the development of heart disease prediction models and can support early detection and appropriate intervention strategies

Sistem Klasifikasi Tahu Non-Formalin Menggunakan Metode Random Forest

Tahu formalin adalah salah satu jenis makanan yang mengandung bahan-bahan kimia yang dapat mengawetkan daripada tahu tanpa formalin. Penelitian ini bertujuan untuk mengklasifikasikan tahu formalin dan tahu tidak formalin. Penelitian ini menggunakan metode random forest yang merupakan bagian dari algoritma machine learning untuk klasifikasi, Penelitian ini mencoba menerapkan metode random forest pada dataset tahu formalin dengan jumlah dataset public. Setelah dilakukan beberapa tahapan dalam pengujian dengan metode random forest maka diperolah hasil akurasi 89%. Model random forest dikembangkan menjadi aplikasi web deteksi tahu non formalin dan tahu formalin yang berfungsi bagi masyarakat dalam meningkatkan pangan agar bebas konsumsi tahu non formalin

PENERAPAN MODEL PEMBELAJARAN KOOPERATIF TIPE MAKE A MATCH DALAM MENINGKATKAN KETERAMPILAN PROSES SAINS SISWA KELAS VIII MTS AINUL YAQIN KOTA JAMBI

Penelitian ini mengkaji masalah yang berkaitan dengan Penerapan model pembelajaran Kooperatif tipe Make a Match dalam meningkatkan keterampilan proses sains siswa kelas VIII MTs Ainul Yaqin Kota Jambi. Pendekatan dan metode penelitian ini adalah penelitian tindakan kelas dengan 3 siklus, setiap siklus terdiri dari 4 tahap yaitu, tahap perencanaan, pelaksanaan, pengamatan, dan refleksi. Sedangkan pengumpulan data dilakukan di kelas VIII dengan jumlah 21 orang siswa. Di Sekolah MTs Ainul Yaqin Kota Jambi penelitian ini menemukan bahwa penerapan model kooperatif tipe Make a Match dalam meningkatkan keterampilan proses sains siswa pada mata pelajaran ilmu pengetahuan alam di kelas VIII. Penelitian ini menunjukan bahwa keterampilan proses sains dalam proses pembelajaran yang diukur dengan evaluasi pada siklus I, siklus II, dan siklus III dengan jumlah nilai rata-rata ketuntasan keterampilan proses sains siswa pada siklus I 19%, siklus II 28,5%, dan siklus III 90,4%. Berdasarkan hasil penelitian tersebut dapat disimpulkan bahwa penerapan pembelajaran kooperatif tipe make a match pada mata pelajaran ilmu pengetahuan alam terpadu khususnya materi fisika siswa kelas VIII di MTs Ainul Yaqin Kota Jambi

Triangular Fuzzy Time Series for Two Factors High-order based on Interval Variations

Fuzzy time series (FTS) firstly introduced by Song and Chissom has been developed to forecast such as enrollment data, stock index, air pollution, etc. In forecasting FTS data several authors define universe of discourse using coefficient values with any integer or real number as a substitute. This study focuses on interval variation in order to get better evaluation. Coefficient values analyzed and compared in unequal partition intervals and equal partition intervals with base and triangular fuzzy membership functions applied in two factors high-order. The study implemented in the Shen-hu stock index data. The models evaluated by average forecasting error rate (AFER) and compared with existing methods. AFER value 0.28% for Shen-hu stock index daily data. Based on the result, this research can be used as a reference to determine the better interval and degree membership value in the fuzzy time series.

The cross-association relation based on intervals ratio in fuzzy time series

The fuzzy time series (FTS) is a forecasting model based on linguistic values. This forecasting method was developed in recent years after the existing ones were insufficiently accurate. Furthermore, this research modified the accuracy of existing methods for determining and the partitioning universe of discourse, fuzzy logic relationship (FLR), and variation historical data using intervals ratio, cross association relationship, and rubber production Indonesia data, respectively. The modifed steps start with the intervals ratio to partition the determined universe discourse. Then the triangular fuzzy sets were built, allowing fuzzification. After this, the FLR are built based on the cross association relationship, leading to defuzzification. The average forecasting error rate (AFER) was used to compare the modified results and the existing methods. Additionally, the simulations were conducted using rubber production Indonesia data from 2000-2020. With an AFER result of 4.77%<10%, the modification accuracy has a smaller error than previous methods, indicating  very good forecasting criteria. In addition, the coefficient values of D1 and D2 were automatically obtained from the intervals ratio algorithm. The future works modified the partitioning of the universe of discourse using frequency density to eliminate unused partition intervals

Peningkatan Kompetensi Guru Madrasah Ibtidayah Duren dan Sabilul Huda Bandungan melalui Pelatihan Pembelajaran Berbasis Teknologi Informasi

Madrasah Ibtidaiyah (MI) Duren Village and Sabilul Huda Jimbaran Bandungan District Semarang Regency want to produce quality graduates. However, the competence of teachers is still conventional learning aids so the learning process is not optimal. To answer this problem, the Department of Informatics, Faculty of Engineering at Universitas Muhammadiyah Semarang, Indonesia proposed information technology-based learning training activities for madrasah teachers. The purpose program is to strengthen human resources for teachers in MI Desa Duren and Sabilul Huda Jimbaran. The proposed program is divided into three learning schemes, (1) interactive presentation media, (2) online classroom learning, and (3) online learning evaluation. The results of this program are that the participants proved to be able to produce effective, elaborative, and interactive teaching materials based on information technology so that students are not bored and enthusiastic about following lessons in the classroom. It can be cancluded the program with the theme "Strengthening Teacher Competencies Through Information Technology-Based Learning Training" can overcome problems in Madrasah Ibtidaiyah (MI) Duren Village and Sabilul Huda Jimbaran Bandungan District Semarang Regency

Three stages algorithm for finding optimal solution of balanced triangular fuzzy transportation problems

In the literature, the fuzzy optimal solution of balanced triangular fuzzy transportation problem is negative fuzzy number. This is contrary to the constraints that must be non-negative. Therefore, the three stages algorithm is proposed to overcome this problem. The proposed algorithm consist of segregated method with segregating triangular fuzzy parameters into three crisp parameters. This method avoids the ranking technique. Next, total difference method is used to get initial basic feasible solution (IBFS) value based on segregating triangular fuzzy parameters. While, modified distribution algorithm is used to determine optimal solution based on IBFS velue. In order to illustrate the proposed algorithm is given the numerical example and based on the result comparison, the proposed algorithm equality to the two existing algorithms and better then the one existing algorithm. The proposed algorithm can solve in the fuzzy decision-making problems and can also be extended to an unbalanced fuzzy transportation problem

Pelatihan Kecerdasan Artifisial (KA) kepada Guru SD di Kabupaten Blora Jawa Tengah untuk Peningkatan Kemampuan di Bidang Digital

In this era of digitalization, the Government is trying from an early age to introduce programming concepts and Artificial Intelligence, which in the future can be used to support the learning process of elementary school education. This training aims to provide new knowledge about how coding programs are included in the subject matter of elementary school children. The teachers are given lessons on basic things suitable to be applied in class and immediately put into practice their projects. With this method, teachers learn to adapt to the new model, where the material will be taught to students in class. Activities are carried out offline at the Arra Cepu Hotel. Stages of Training through Presentations, Videos, and Quizzes. In this activity, the teachers gain knowledge and skills in coding programs for beginners; pre-test and post-test will be used as a measure to assess it. In the future, teachers will experiment with coding lessons with students. An early introduction to programming and artificial intelligence for elementary school teachers is expected to positively impact students, mainly in their ease of receiving and understanding a lesson