Application of Data Mining for Rainfall Prediction Classification in Australia with Decision Tree Algorithm and C5.0 Algorithm

Tujuan: Penelitian ini bertujuan untuk memprediksi hujan di Australia dengan pendekatan klasifikasi machine learning. Prediksi hujan yang tepat dan akurat sangat penting untuk perencanaan dan pengelolaan sumber daya air, peringatan banjir, kegiatan konstruksi dan operasi penerbangan serta yang lainnya.Perancangan/metode/pendekatan: Metode atau tahapan yang diterapkan dalam melakukan klasifikasi prediksi hujan di Australia yaitu melalui beberapa tahapan diantaranya Pengumpulan Data, Data Pre-processing (termasuk dilakukan penanganan Missing Value didalamnya), Pemodelan Klasifikasi dengan menerapkan dan membandingkan algoritme Decision Tree dan C5.0, Validasi Hasil menggunakan Partisi Dataset dan k-Cross Fold Validation serta Evaluasi Model menggunakan Confussion Matrix.Hasil: Berdasarkan hasil yang diperoleh, evaluasi menggunakan 10-Cross Fold Validation lebih unggul yang memiliki akurasi paling tinggi sebesar 87.35% untuk algoritme Decision Tree dan akurasi sebesar 86.85% untuk algoritme C5.0 Rule-Based Model, dibandingkan dengan metode Split 80:20 pada kasus prediksi hujan di Australia.Keaslian/state of the art: Selain model klasifikasi yang digunakan, validasi dataset baik itu dengan partisi dataset atau k-Cross Fold Validation juga dapat mempengaruhi akurasi hasil prediksi

Rain Prediction Clustering in Australia Using the K-Means Algorithm in the WEKA and RStudio Application

Purpose: The purpose of this study is how to create an ideal cluster in predicting rainfall in Australia based on the percentage of the sum of squares error (SSE) using the K-Means algorithm with WEKA and RStudio applications.Design/methodology/approach: The method or stages applied in predicting rain in Australia are through several stages including Data Collection, Data Pre-processing (including Missing Value handling in it), Data Mining Modeling by applying the K-Means Clustering algorithm using WEKA and RStudio, Validation results with SSE as well as Data Visualization using plots.Findings/result: Based on the results obtained, clusters of 2 with an SSE of 28.0% are ideal clusters for predicting rain in Australia. In the WEKA software, rain clusters are represented by blue nodes, and non-rainy clusters are represented by red nodes. While in the RStudio software, rain clusters are represented by black nodes and non-rainy clusters are represented by red nodes.Originality/value/state of the art: Get the ideal cluster in predicting rainfall in Australia by comparing the results obtained using the WEKA and RStudio applications

Perbandingan Kinerja Algoritma K-Nearest Neighbor, Naïve Bayes Classifier dan Support Vector Machine dalam Klasifikasi Tingkah Laku Bully pada Aplikasi Whatsapp

WhatsApp is the most popular messaging application in Indonesia. This causes the emergence of cyberbullying behavior by its users. This study aims to classify WhatsApp chat to two classes, namely bully and not bully. The classification algorithms used are k-NN, NBC and SVM. The results show that the SVM algorithm is better at solving this case with an accuracy of 81.58%

CLASSIFICATION OF CUSTOMER COMPLAINTS ON INSTAGRAM COMMENTS USING NAÏVE BAYES ALGORITHM WITH N-GRAM FEATURE EXTENSION

Customer complaints about the company can be used as a form of self-evaluation and performance that has been carried out by the company, based on customer complaints the company can find out the weaknesses that exist in the company and fix them. The forms of submitting customer complaints are very diverse, currently not only by telephone, but customers also submit suggestions or complaints, customers can submit suggestions or complaints via electronic mail or e-mail or forums in cyberspace that are indeed created by product-producing companies to accommodate various complaints, suggestions, and direct criticism from consumers, especially social media that are free to express opinions on the delivery services used. Instagram is a social media that is more inclined towards images and on the other hand, has captions and comments text, a study is needed for the problem of customer complaints from shipping service users on an Instagram account of a delivery service company. Based on this background, a solution is needed in solving problems for text mining classification using Naïve Bayes with SMOTE techniques and N-Gram feature extraction with the usual process for text mining so that it can produce Naïve Bayes and SMOTE accuracy with an accuracy of 88.54%, before implementation. N-Gram and the accuracy rate increased by 1.44% after the N-Gram Term was applied to 89.98% by using a dataset of 776 Instagram comment text records that had to preprocess text

Pengadaan dan Pemasangan Instalasi Listrik Lampu Taman di Perum Rajeg PSP RW02 Kecamatan Rajeg Kabupaten Tangerang

In the world of education, every lecturer is required to do the Tri Dharma of higher education. One of the Tri Dharma of higher education is PKM (Community Service) activities. With this background, our lecturer and student team want to help the community. Perum PSP Rajeg is the northern part of Tangerang. In this housing, there is a garden area that is poorly maintained. Poorly maintained gardens are caused by poor lighting. The lighting is not good because there are no lights in the garden area. The dark area of the park causes people to dislike exercising in the park. If people do not like to exercise, the health level of the community is not optimal. If the community's health level is not optimal, it can easily be attacked by diseases. Because of this, our team of lecturers and students were moved to impose PKM on PSP Rajeg. The implementation of this community service activity is carried out by the method of conducting location surveys, discussing with the community about the needs they want to need. After conducting a location survey, it was found that the PSP Rajeg community needed lighting in the garden area. This is useful for community activities when exercising in the park. The installation of lights in the parking area is carried out between lecturers, students and community members. Installation of lights in the garden area is carried out with a total of 7 light points and poles, one light point uses a 25-watt energy-saving lamp. After carrying out this Community Service activity, the community members are happier to do sports in the parking area, so that the level of community health becomes better and is more resistant to various kinds of diseases that can attack. The community of PSP RW02 Rajeg, Rajeg sub-district, Tangerang district is very supportive and grateful for the implementation of this PKM activity

SENTIMENT ANALYSIS ON TWITTER OF PSBB EFFECT USING MACHINE LEARNING

A collection of tweets from Twitter users about PSBB can be used as sentiment analysis. The data obtained is processed using data mining techniques (data mining), in which there is a process of mining the text, tokenize, transformation, classification, stem, etc. Then calculated into three different algorithms to be compared, the algorithm used is the Decision Tree, K-NN, and Naïve Bayes Classifier to find the best accuracy. Rapidminer application is also used to facilitate writers in processing data. The highest results from this study were the Decision Tree algorithm with an accuracy of 83.3%, precision 79%, and recall 87.17%

Pengembangan Aplikasi Sistem Informasi Geografis Untuk Monitoring Gempabumi

This Research conducted to develop geographic information system (GIS) that can be used to manage data and visualize the earthquake information. The research methodology consisted of collecting spatial data and non spatial data and system design. System design consists of designing the flow of data, designing databases, designing menus and design screens. The conclusion that can be drawn from this study is that GIS is developed to display information about how the earthquake that has been and is happening with magnitude parameters in a visual form suitable conditions. GIS is designed to use its own database and have the capacity to perform spatial data processing and non-spatial to generate a tsunami early warning system. This system can also simplify and accelerate the delivery of earthquake information to the public

PELATIHAN PEMASANGAN DAN PERAWATAN AUDIO SYSTEM DI MUSHOLA BAITURROHMAN, TAMBORA-JAKBAR

Masjid dan musholla adalah tempat beribadah untuk umat islam. Peralatan pendukungnya antara lain audio system. Audio system digunakan untuk mengumandangkan adzan serta iqomah. Bagian pendukung dari peralatan audio system, antara lain adalah amplifier, speaker, microphone dan kabel. Amplifier berguna untuk mengatur suara yang keluar ke speaker. Amplifier dilengkapi dengan pengaturan keseimbangan suara, bass dan treble yang masing-masing digunakan untuk memperjelas suara. Speaker digunakan untuk audio system di dalam maupun luar mushola. Microphone digunakan untuk penghubung dari amplifier ke speaker. Musholla Baiturrohman adalah salah satu tempat ibadah yang terletak di daerah Tambora-Jakarta Barat. Setelah melakukan survei di Musholla Baiturrohman, mahasiswa dan dosen melihat peralatan audio system yang sudah tidak berfungsi. Amplifier sudah tidak bisa dikontrol, microphone tidak mengeluarkan suara ke speaker dan instalasi listrik yang tidak aman dari jangkauan manusia. Kami melakukan pelatihan pemasangan dan perawatan audio system kepada para jamaah Musholla Baiturrohman. Pelatihan ini untuk meningkatkan kemampuan jamaah atau juga menghasilkan para ahli pemasangan dan perawatan audio system untuk berwirausaha. Metode pelatihan dengan ceramah, diskusi dan praktikum pemasangan audio system di Musholla Baiturrohman. Pelatihan perawatan juga dilakukan pembimbingan agar jamaah bisa menjaga lifetime dari perangkat audio system yang terpasang. Audio system Musholla Baiturrohman telah terpasang dan berfungsi dengan baik. Para jamaah mengikuti pelatihan ini dengan baik dan antusia

PENGEMBANGAN APLIKASI SISTEM INFORMASI GEOGRAFIS UNTUK MONITORING GEMPABUMI

This Research conducted to develop geographic information system (GIS) that can be used to manage data and visualize the earthquake information. The research methodology consisted of collecting spatial data and non spatial data and system design. System design consists of designing the flow of data, designing databases, designing menus and design screens. The conclusion that can be drawn from this study is that GIS is developed to display information about how the earthquake that has been and is happening with magnitude parameters in a visual form suitable conditions. GIS is designed to use its own database and have the capacity to perform spatial data processing and non-spatial to generate a tsunami early warning system. This system can also simplify and accelerate the delivery of earthquake information to the public

Double Exponential Smoothing Berimputasi LOCF Dan Linear Interpolation Dalam Akurasi Peramalan Harga Harian Emas

Gold is another kind of investment that often experiences price change, mostly every day. Because of its price fluctuation, forecasting is needed to help the investor in investment decision making. But during this Coronavirus Disease 2019 (Covid-19), the gold price is fluctuating extremely than the past 4 years so a better forecasting method approached and analysis technique is needed due to this case. Double Exponential Smoothing method is chosen to forecast this daily gold price. On the other hand, there are so many missing values spreading around the main dataset so the imputation method is needed too, Last Observation Carried Forward (LOCF) and linear interpolation are chosen for imputing the missing values. In this research, the main dataset was split into 3 (three) datasets, which are Precovid-19 (before Covid-19, used only for visualizing the actual fluctuation condition during this pandemic), Incovid-19 (during Covid-19 based on the date where the first Covid-19 case occurred in Indonesia), and Combination (a binding dataset of Pracovid-19 and Incovid-19). Although Incovid-19’s MAPE value is higher than Pracovid-19 and Combination’s MAPE values, in evaluation session showed that Incovid-19’s MAPE of forecast results has the lowest value rather than Combination’s MAPE of forecast results, so the conclusion of this research is Incovid-19 dataset with LOCF imputation is the most adaptive with the actual condition and it is used to forecast the daily gold price until the last period of the main dataset then