Pembobotan Kata Berbasis Preference Untuk Perangkingan Dokumen Fiqih Berbahasa Arab

Dalam pencarian, selain kesesuaian query dengan hasil pencarian, terdapat penilaian subjektif pengguna yang diharapkan turut menjadi faktor penentu dalam perangkingan dokumen. Aspek preferensi tersebut tampak pada pencarian dokumen fiqih. Seseorang cenderung mengutamakan metodologi fiqih tertentu meskipun tidak mengabaikan pendapat metodologi fiqih lain. Faktor preferensi menjadi hal yang diperlukan selain relevansi dalam perangkingan dokumen. Oleh karena itu, pada penelitian ini diajukan metode pembobotan kata berbasis preferensi untuk merangkingkan dokumen sesuai dengan preferensi pengguna. Metode yang diajukan digabungkan dengan pembobotan kata berbasis indeks dokumen dan buku sehingga mampu memperhatikan aspek kesesuaian (relevance) dan keutamaan (preference). Metode pembobotan yang diusulkan disebut dengan Invers Preference Frequency with α value (IPFα). Langkah pembobotan yang diusulkan yaitu dengan perhitungan nilai preferensi term dengan pembobotan IPF. Kemudian nilai preferensi dari term dokumen yang sama dengan term query dikalikan denga

Modul praktikum struktur data

Modul Praktikum Struktur Data pada Program Studi Teknik Informatika Fakultas Sains dan Teknologi UIN Maulana Malik Ibrahim Malang. Menggunakan bahasa pemrograman Java dan mencangkup pembahasan 10 pembahasan yaitu tentang Arrays, Simple Sorting, Stack and Queue, Linked List, Recursion, Advance Sorting, Middle Test, Binary Tree, Hash Table, Heap, Graph

Modul praktikum struktur data

Modul Praktikum Struktur Data pada Program Studi Teknik Informatika Fakultas Sains dan Teknologi UIN Maulana Malik Ibrahim Malang. Menggunakan bahasa pemrograman Java dan mencangkup pembahasan 10 pembahasan yaitu tentang Arrays, Simple Sorting, Stack and Queue, Linked List, Recursion, Advance Sorting, Middle Test, Binary Tree, Hash Table, Heap, Graph

Perbandingan Metode Machine Learning dalam Analisis Sentimen Twitter

Perbedaan pemahaman di kalangan masyarakat sering terjadi terkait diterbitkannya kebijakan baru oleh pemerintah. Diantaranya adalah kebijakan dalam menangani kasus kekerasan seksual di lingkungan kampus yang tertulis dalam Peraturan Menteri Pendidikan, Kebudayaan, Riset dan Teknologi Nomor 30 Tahun 2021 sehingga diperlukan kajian mendalam dengan melakukan analisis sentimen. Ada banyak algoritma yang digunakan dalam penelitian analisis sentimen, maka dalam penelitian ini peneliti menggunakan 4 algoritma klasifikasi machine learning, yaitu Support Vector Machine, K-Nearest Neighbor, Naïve Bayes Classifier, dan Logistic Regression untuk dilakukan perbandingan performa dari masing-masing algoritma. Data penelitian yang digunakan berjumlah 470 data dengan pembagian 236 tweet berlabel positif dan 238 tweet berlabel negatif yang diambil pada rentang bulan Oktober sampai Desember. Dalam penelitian ini menggunakan perangkat lunak RapidMiner dengan menerapkan teknik k-Fold Cross Validation untuk memisahkan data latih dan data uji secara acak. Terdapat perbedaan performa pada algoritma machine learning yang digunakan untuk analisis sentimen, dari algoritma yang telah diujikan, nilai akurasi tertinggi terdapat pada algoritma Support Vector Machine, yaitu sebesar 69,15%, kemudian nilai presisi tertinggi terdapat pada algoritma K-Nearest Neighbor, sebesar 69,07%, kemudian nilai recall tertinggi terdapat pada algoritma Support Vector Machine sebesar  71,98%, dan nilai f-measure tertinggi terdapat pada algoritma K-Nearest Neighbor yaitu sebesar 68,08%

Analisis sentimen terhadap PERMENDIKBUD No. 30 pada media sosial Twitter menggunakan metode Naive Bayes dan LSTM

Research on Sentiment Analysis of public responses to PERMENDIKBUD No. 30 on Twitter social media can use Machine Learning and Deep Learning models. This study uses two methods derived from the two models, namely the Naïve Bayes method and the Long Short-Term Memory method. Data collection by crawling data using the Twitter API which uses keywords in the form of "permendikbud30" and "Sexual violence on campus". contains "Negative" and "Positive" However, the dataset that has been preprocessed is reduced to 471 data. After preprocessing is done, then the weighting process is carried out using the TF-IDF method and continued with the calculation method. The results of this study indicate that the LSTM method gets a higher performance value, namely the Accuracy value of 77%, Precision of 84%, Recall of 75%, and F1-Score of 80%. Testing the Naïve Bayes method obtained results of 76% accuracy, 75% precision, 75% recall value and 75% F1-Score

WebGIS of mapping Pasuruan city furniture industry using Leaflet and Openstreetmap

Pasuruan is a well-known area for producing furniture products in East Java. The furniture commodity industry is widely spread in this city. This industry is also one of the top products. To boost market interest in the furniture industry in Pasuruan City, which has been affected by the COVID-19 pandemic, and a geographic information system has not yet been developed in an effort to develop the furniture industry, research has been carried out on a web-based geographic information system (WebGIS) mapping the furniture industry in Pasuruan City using Leaflets and OpenStreetMap. The developed furniture industry WebGIS application takes advantage of the open source and dynamic advantages of Leflet and OpenStreetMap technologies which have provided very decent results on several GIS web-based application systems. In the testing phase with the Blackbox method, the WebGIS application for the furniture industry in Pasuruan City functionally has run well on desktop devices and also run well on simulated mobile devices. Testing was carried out on several types of browsers like Google Chrome and Microsoft Edge. This WebGIS application has been able to map the furniture industry in Pasuruan City so that the distribution of economic activity actors, especially the furniture industry, can be seen throughout the city and each sub-district. It can provide detailed information on furniture industry players in Pasuruan

PREFERENCE BASED TERM WEIGHTING FOR ARABIC FIQH DOCUMENT RANKING

In document retrieval, besides the suitability of query with search results, there is also a subjective user assessment that is expected to be a deciding factor in document ranking. This preference aspect is referred at the fiqh document searching. People tend to prefer on certain fiqh methodology without rejecting other fiqh methodologies. It is necessary to investigate preference factor in addition to the relevance factor in the document ranking. Therefore, this research proposed a method of term weighting based on preference to rank documents according to user preference. The proposed method is also combined with term weighting based on documents index and books index so it sees relevance and preference aspect. The proposed method is Inverse Preference Frequency with α value (IPFα). In this method, we calculate preference value by IPF term weighting. Then, the preference values of terms that is equal with the query are multiplied by α. IPFα combined with the existing weighting methods become TF.IDF.IBF.IPFα. Experiment of the proposed method uses dataset of several Arabic fiqh documents. Evaluation uses recall, precision, and f-measure calculations. Proposed term weighting method is obtained to rank the document in the right order according to user preference. It is shown from the result with recall value reach 75%, precision 100%, and f-measure 85.7% respectively

MITIGASI COVID 19 MELALUI PELATIHAN MEMBUAT HAND SANITIZER TAKMIR MASJID SEBAGAI UPAYA MINIMALISASI PENYEBARAN VIRUS CORONA DI KLASTER TEMPAT IBADAH

Abstrak: Di era new normal, kebutuhan hand sanitizer penting untuk mencegah penyebaran virus corona, sehingga perlu diadakan pelatihan pembuatan hand sanitizer sesuai standar WHO. Tujuan pengabdian ini adalah memberikan pelatihan kepada takmir masjid untuk membuat hand sanitizer secara mandiri. Metode pengabdian menggunakan Participatory Action Research (PAR) yang terdiri dari plan, action dan refleksi. Peserta pelatihan adalah takmir masjid di Kelurahan Purwantoro sebanyak 50 orang. Hasil dari pengabdian ini adalah 1) perencanaan dilakukan dengan pihak kelurahan Purwantoro dan kepala rukun warga (RW) 5 terlaksana dengan baik. 2) Tahap action terlaksana dengan baik. Hasil pengamatan yang dilakukan saat praktik membuat hand sanitizer sebanyak 75% takmir masjid mampu membuat hand sanitizer. 3) Refleksi dilakukan dengan pendistribusian hand sanitizer dan masker ke masjid-masjid di Purwantoro.Abstract:  In the new normal era, the need for Hand Sanitizers to prevent the spread of the COVID-19 pandemic is urgently needed, so training on making hand sanitizers is needed according to WHO standards. The purpose of this service is to provide training to the manager’s mosque to make hand sanitizers independently. The service method uses Participatory Action Research (PAR) which consists of plan, action and reflection. The training participants are manager’s mosque in Purwantoro Village as many as 50 people. The results of this service are 1) the planning carried out with the Purwantoro village and the head of the community unit (RW) 5 is carried out well. 2) The action stage was carried out well. The results of observations made during the practice of making hand sanitizers were 75% of manager’s mosque were able to make hand sanitizers. 3) Reflection is done by distributing hand sanitizers and masks to mosques in Purwantoro

CRISP-DM method on Indonesian micro industries (UMKM) using K-Means clustering algorithm

UMKM plays an important role in supporting the economy in Indonesia. As one of the steps to reduce poverty, the govwordernment should pay more attention to the growth of its UMKM based on existing data. Data of UMKM collected from 2014 to 2018 in several economic sectors such as the leather industry, Metal Industry, Woven Industry, Pottery Industry, Fabric Industry, Food and Beverage Industry, and Other Industry can be used as government guidelines in efforts to solve poverty problems by processing them using k-means algorithm. The research was carried out using the CRISP-DM method and K-Means algorithm to determine the cluster of provinces so that the policy or decision making can be made more wisely. By using RapidMiner, data processing can be done quickly. The result of the study shows that DBI values of each data using 5 as k are 0.308, 0.312, 0.259, 0.272, 0.333, 0.369, 0.289, and 0.266. Based on that, Jawa Timur and Jawa Tengah have a large industrial growth while Jawa Barat seems to start leaving traditional industries. Besides, the other provinces' industrial growth appears to be stable. It is expected that the government would make wise policies to support the growth of UMKM in Indonesia

Diagnosis Penyakit Jantung Menggunakan Adaptive Neuro-Fuzzy Inference System (ANFIS)

The number of uncertain risk factor in heart disease makes experts difficult to diagnose its disease. Computer technology in the health field is mostly used. In this paper, we implement a system to diagnose heart disease. The used method is Adaptive neuro-fuzzy inference system which combine the advantage of fuzzy and neural network. The used data is UCI Cleveland data that have 13 attributes as inputs. Output system diagnosis compared with observational data for evaluation. System performance tested by calculating accuracy. Tests were also conducted on the variation of the learning rate, iteration, minimum error, and the use of membership functions. Accuracy obtained from test is 65,657% where using membership function Beta