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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Informatika dan Teknik Elektro Terapan Infotech Journal Journal of Information System Jurnal Teknologi dan Sistem Komputer Jurnal Informatika JURNAL LENTERA : Kajian Keagamaan, Keilmuan dan Teknologi JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Jurnal TAM (Technology Acceptance Model) JURNAL ILMIAH INFORMATIKA ILKOM Jurnal Ilmiah DoubleClick : Journal of Computer and Information Technology INTECOMS: Journal of Information Technology and Computer Science Jiko (Jurnal Informatika dan komputer) Jurnal ULTIMATICS Jurnal Informasi dan Komputer STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Progresif: Jurnal Ilmiah Komputer Jurnal Pendidikan dan Konseling Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak Journal of Applied Engineering and Technological Science (JAETS) Generation Journal JATI (Jurnal Mahasiswa Teknik Informatika) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Wahana Pendidikan Paradigma

Betha Nurina Sari

Program Studi Informatika, Universitas Singaperbangsa Karawang

Author-ID : 6597298

Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

Published : 52 Documents Claim Missing Document

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Sistem Pakar Penentu Profil Risiko Investasi Adam Mukti Wibisono; Betha Nurina Sari
JOINS (Journal of Information System) Vol 7, No 1 (2022): Edisi Mei 2022
Publisher : Universitas Dian Nuswantoro

Investasi dapat didefinisikan sebagai penanaman sejumlah uang atau sumber daya lainnya pada masa sekarang dengan harapan pengembalian di masa yang akan datang. Dalam praktiknya, investasi sering dikaitkan dengan berbagai aktivitas yang berkaitan dengan menginvestasikan dana pada berbagai alternatif aset, baik berupa aset fisik seperti tanah, emas, properti, atau aset finansial, seperti berbagai bentuk surat. Seperti saham, obligasi atau reksa dana. Dengan banyaknya instrumen investasi yang ada sekarang masyarakat tentu kesulitan menentukan investasi mana yang cocok dengan profil risikonya. Pada penelitian ini akan mengembangkan sebuah sistem pakar untuk menentukan profil risiko investasi seseorang menggunakan metode forward chaining, metode ini dilaksanakan dengan mengumpulkan data atau fakta-fakta yang dibutuhkan terlebih dahulu lalu di definisikan aturan-aturannya, kemudian disimpulkan untuk memberikan solusi yang tepat. Dari hasil penelitian ini seseorang dapat mengetahui profil risiko nya dalam berinvestasi, mengetahui instrumen investasi apa yang cocok, alokasi dana yang tepat dan juga jangka waktu yang terbaik sesuai dengan profil risiko nya Kata kunci: investasi, profil risiko, forward chaining, sistem pakar

Application of C5.0 Algorithm in Prediction of Learning Outcomes in Calculus Subject Fida Nafisah Giustin; Betha Nurina Sari; Tesa Nur Padilah
Journal of Applied Engineering and Technological Science (JAETS) Vol. 3 No. 2 (2022): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Calculus is one of the basic subject that must be studied at the computer science faculty of the informatics engineering study program. For some students, especially in the Faculty of Informatics Engineering, calculus is a subject that is considered quite difficult, even though this subject is important for them. And the resulted for some students having to repeat this subject. For this reason, predictions of calculus learning outcomes are carried out by applying the data mining process and using the C5.0 method for the prediction process based on the classification concept that will be carried out. This study applies the Cross Industry Standard Process for Data Mining (CRISP – DM) methodology with the C5.0 algorithm. The results are in the form of a decision tree (Decision tree) and the rules in it using the attributes of guardian, number of family members, status of residence, internet, activity, desire to continue study, the last education of parents (father and mother), parents' occupations, grades on assignments, UAS, and UTS. The C5.0 algorithm is able to predict the results of learning calculus. The evaluation results show that the applied C5.0 algorithm has an accuracy of 95%.

Analisis Komparasi Algoritma Dalam Prediksi Indeks Harga Saham Gabungan (IHSG) Febriandika Dian Nurcahyo; Rizal Fadilah; Betha Nurina Sari
Jurnal Ilmiah Wahana Pendidikan Vol 8 No 11 (2022): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

JKSE is an index that can describe the condition and stability of the Indonesian economy. JKSE has a very fast movement and has large fluctuations because the JKSE is a joint stock so predicting its movement can be a little difficult. Due to the difficulties in forecasting the JKSE stock price, the right algorithm is needed to have accurate forecasting results. The results of the three algorithms will be compared using T-Test to evaluate the performance of the three algorithms in predicting the movement of the JKSE stock. The results of the comparison between linear regression algorithms, neural networks, and support vector machines, it is found that the neural network algorithm has the best performance with an RMSE value of 14,660. Then the backward elimination method was used in the model, it was found that the performance of the algorithm in that model had an increase, except for the linear regression algorithm. The neural network algorithm plus the backward elimination method is the best algorithm with an RMSE value of 10,895.

Pemanfaatan Sistem Pakar untuk Mendiagnosis Penyakit pada Burung Murai Batu Ilyas Shiddiq Pratama; Iskandar Zulkarnaen; Audy Sukma Putera; Betha Nurina Sari
Paradigma Vol. 24 No. 1 (2022): Periode Maret 2022
Publisher : LPPM Universitas Bina Sarana Informatika

The stone magpie has many enthusiasts among the community and breeders, but the disease suffered by the stone magpie is a serious problem. There are various diseases that attack and cause farmers to be unable to diagnose and difficult to know the type of disease, because knowledge about these diseases is still limited in diagnosing diseases that attack stone magpies. To diagnose the type of disease, you can use this method, you can find out the symptoms of the disease, then you can determine the diagnosis of the disease and take early prevention so that the disease does not spread. Therefore, we need a system that is able to diagnose the type of disease, so that breeders or the public can find out early on the types of diseases that will attack new magpies and anticipate transmission to other animals. The results of making this expert system will display symptoms that occur, diagnosis, and prevention so that they are not contagious, which can help breeders and the public to obtain information on types of diseases and be able to assist in maintaining the health of stone magpies.

PENERAPAN ALGORITMA K-NEAREST NEIGHBOR DALAM KLASIFIKASI JUDUL BERITA HOAX Muhammad Diki Hendriyanto; Betha Nurina Sari
JURNAL ILMIAH INFORMATIKA Vol 10 No 02 (2022): Jurnal Ilmiah Informatika (JIF)
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jif.v10i02.5477

With the rapid development of information technology, especially in Indonesia, information is more easily obtained through online media. Therefore, the dissemination of information in online media becomes uncontrollable and a lot of information is not in accordance with the facts or can be said to be a hoax. Readers should be more careful when reading news headlines to avoid hoaxes. The purpose of this research is to find out how to apply the K-Nearest Neighbor (KNN) algorithm in classifying news including hoaxes or not hoaxes. In the process, the classification of hoaxes or non-hoaxes uses the KDD method in text mining and goes through several stages, namely preprocessing, word weighting with TF-IDF and classification using the KNN algorithm. There are 3 scenarios in the data split process, namely 90:10, 80:20, and 70:30. Evaluation is done by using a confusion matrix. The results of this study obtained the highest accuracy of 93.33% with a k value of 3 in the 90:10 scenario. So, the K-Nearest Neighbor algorithm is suitable for classifying hoax news titles.

Prediksi Rating Game Menggunakan Algoritme C4.5 Berdasarkan Entertainment Software Rating Board Rahmat Alfanza; Sani Shalihamidiq; Ratna Mufidah; Betha Nurina Sari
Progresif: Jurnal Ilmiah Komputer Vol 19, No 1: Februari 2023
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v19i1.977

Games can be played by all ages including children. If the game being played is not in accordance with the child's developmental period, it will have a negative impact on the child. Therefore, the rating on the game is very influential because if there is an error in rating the game, minors can play games that are not in accordance with their developmental needs. The purpose of this research is to create a machine learning model to predict game ratings using data from the ESRB (Entertainment Software Rating Board). This study uses the C4.5 classification algorithm and the python programming language. The data used in this study is game rating data taken from 2020 to 2022. The results of this study indicate that the machine learning model created can predict game ratings with a ratio of 70% training data and 30% testing data, with an accuracy rate of 86%. Keywords: Game; Data Mining; Classification; Algorithm C4.5 AbstrakGame dapat dimainkan oleh semua kalangan usia termasuk usia anak-anak. Jika game yang dimainkan tidak sesuai dengan masa kembang anak maka akan berdampak negatif kepada anak. Oleh sebab itu rating pada game sangat berpengaruh karena apabila terjadi kesalahan terhadap pemberian rating pada game anak dibawah umur dapat memainkan game yang tidak sesuai dengan kebutuhan tumbuh kembangnya. Tujuan penelitian ini adalah membuat model machine learning untuk memprediksi rating pada game dengan menggunakan data dari ESRB (Entertainment Software Rating Board). Penelitian ini menggunakan algoritme klasifikasi C4.5 dan bahasa pemrograman python. Data yang digunakan pada penelitian ini adalah data rating game yang diambil dari tahun 2020 sampai 2022. Hasil dari penelitian ini menunjukkan model machine learning yang dibuat dapat memprediksi rating game dengan perbandingan 70% data training dan 30% data testing, dengan tingkat akurasi sebesar 86%.Kata kunci: Game; Data Mining; Klasifikasi; Algoritme C4.5

Penerapan Naïve Bayes untuk Klasifikasi Kriteria Air Layak Minum dengan Metode CRISP-DM Ibnu Alfitra Salam; Katon Wahyudi Putra; Sisca Yuliatina; Betha Nurina Sari
Paradigma Vol. 25 No. 1 (2023): March 2023 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v25i1.1754

With water, living things can do various things easily. The adequacy of water is also important in maintaining human health. Water can be said to be feasible if its content is in accordance with the feasible criteria. From the dataset obtained regarding the feasibility of water for this study, it will calculate the accuracy value obtained using the Naive Bayes algorithm. To simplify the process of processing research data this time using the CRISP-DM methodology which is a stage for data mining. The study uses two tools, namely Rapidminer and Google Collab to compare their accuracy values. By using the two tools in implementing the Naive Bayes algorithm on a potable water quality dataset, an accuracy of 62.8% is obtained. This value is accurate enough to predict the quality of drinking water.

Application of C4.5 Classification in Improving Recitation Fluency in Students Sisca Yuliantina; Betha Nurina Sari
Paradigma Vol. 25 No. 1 (2023): March 2023 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v25i1.1775

Fluency in reciting the Koran is learning the recitation of the Qur'an in a tartil way. Based on the observations of researchers, the learning of tajwid in recitation and at school has not been effective so far. Because of this, both teachers and students at recitation or at school need improvement by finding out what can increase fluency in reciting the Koran and what has the most influence on improving fluency. So this study aims to improve the fluency of the Koran in students. The research method used is the Decision Tree data mining classification method with the C4.5 algorithm. The results of data processing with the C4.5 algorithm using the Rapidminer tools are attribute C1(fluency) being the most influential attribute for increasing students' reading fluency and performance data obtained with an accuracy of 83,33%.

Penerapan Algoritma K-Means Untuk Pemetaan Penyebaran Penyakit Demam Berdarah (DBD) Pada Kabupaten/Kota Di Jawa Barat Bintang Selviana; Betha Nurina Sari
Jurnal Pendidikan dan Konseling (JPDK) Vol. 5 No. 2 (2023): Jurnal Pendidikan dan Konseling
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jpdk.v5i2.13256

DBD atau biasa disebut Demam Berdarah adalah salah satu penyakit yang seringkali didapati di daerah tropis dan subtropis karena penyebarannya disebabkan oleh nyamuk Aedes Aegypti. Demam berdarah juga masih menjadi salah satu penyakit dengan tingkat penyebaran yang tinggi di Indonesia terutama di daerah Jawa Barat. Tercatat Kota Bandung dan Kabupaten Bogor menjadi kota/kabupaten dengan kasus DBD tertinggi di Indonesia. Dikarenakan hal tersebut perlu adanya analisis terkait peta penyebaran kasus DBD terutama pada wilayah Jawa Barat untuk dapat mengetahui golongan dari setiap daerah dan dapat memudahkan penanganan yang akan dilakukan sesuai dengan golongan dari setiap wilayah tersebut. Metodologi penelitian yang digunakan yaitu Algoritma K-Means clustering dengan bantuan tools Rapidminer dikarenakan algoritma ini menjadi salah satu solusi untuk mengetahui titik penyebaran. Mengimplementasikan Algoritma K-Means clustering dengan membagi menjadi 3 cluster penyebaran yaitu tinggi, sedang dan rendah. Hasil yang diperoleh dari penerapan Algoritma K-Means clustering pada penelitian ini terdapat 1 kabupaten/kota yang menjadi cluster 1, 7 kabupaten/ kota pada cluster 2 dan 19 kabupaten/ kota pada cluster 3 dari total 27 kabupaten/ kota yang berada di Jawa Barat

Implementasi Algoritma CNN Untuk Pemilahan Jenis Sampah Berbasis Android Dengan Metode CRISP-DM Sita Alden; Betha Nurina Sari
Jurnal Informatika Vol 10, No 1 (2023): April 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v10i1.14985

Implementasi algoritma Convolutional Neural Network (CNN) untuk memilah jenis sampah berbasis Android dapat membantu masyarakat dalam memilah sampah dengan benar. Aplikasi ini akan menerima masukan berupa foto sampah yang diambil oleh pengguna dan kemudian menggunakan algoritma CNN untuk mengklasifikasikan jenis sampah. Hasil dari klasifikasi kemudian ditampilkan kepada pengguna sehingga dapat mengetahui jenis sampah dengan akurasi yang tepat untuk dibuang ke tempat sampah sesuai jenisnya. Pada pengujian pemilahan sampah organik dan anorganik berhasil dilakukan dengan menggunakan metode Transfer Learning CNN dengan menerapkan arsitektur Mobile Net. Dataset sampah yang terkumpul adalah sebanyak 5.428 di train di ML Kit. Precision 97,95% dan recall sebesar 95,18%. Pada pengujian menggunakan Android dengan library tensorflow Lite kulit pisang dapat terdeteksi menghasilkan output sampah organik dengan akurasi sebesar 96%. Begitupun dengan sampah kardus dapat terdeteksi menghasilkan output sampah anorganik dengan akurasi sebesar 99%.Implementation of a Convolutional Neural Network (CNN) algorithm for Android-based garbage sorting to help the public sort garbage correctly. The application will accept input in the form of user-taken garbage photos and use the CNN algorithm to classify the type of garbage. The classification results are then shown to the user to help identify the correct type of garbage to dispose of. Testing of organik and inorganik garbage sorting was successfully done using the Transfer Learning CNN method with the Mobile Net architecture. Collected garbage dataset is 5,428 in train in ML Kit, precision is 97.95% and recall is 95.18%. In testing using Android with the tensorflow Lite library, banana peels can be detected with 96% accuracy and cardboard can be detected with 99% accuracy.

Co-Authors ., Garno Aan Rohanah Abimanyu Widhiantoyo Adam Mukti Wibisono Afifa Atira Agil Aditya Agil Aditya Ahmad Khusaeri Ammellia Putri, Zahra Andini Diyah Pramesti Anggri, Anggri Anisa Nur Hasanah Apriade Voutama aqillah Aqillah Fadia Haya Arbizal, Yusril Assyifa Khalif Ato Sugiharto Audy Sukma Putera Aulia Kusnadi, Putri Azhari Ali Ridha Bayu Adhi Pamungkas Bintang Selviana Budi Arif Dermawan Dadang Yusuf Darmawan, Adam Delpidat, Derrenz Dinda Fitriani Duwo Jiwo Saputro, Adi Dwi Latifah Rianti Fathimatuz Zahra Febriandika Dian Nurcahyo Fida Nafisah Giustin garno, Garno Hannie, Hannie Hasanah, Anisa Nur Hayah, Najmil Ibnu Alfitra Salam Ilham, Septian Ilyas Shiddiq Pratama Intan Purnamasari Iqbal Maulana Iskandar Zulkarnaen Ivan Jovian Katon Wahyudi Putra Khalif, Assyifa Lusen, Vieri Luthfiyyah, Ibtihal Qomariyyah Margaretta, Putri Mayasari, Rini Muhamad Khandava Mulyadien Muhammad Diki Hendriyanto Muhammad Fadhlil Aziz Nabilla Audy, Ide Nur Padilah, Tesa Nurhasanah, Desi Nurhusaeni, Muhamad Nurohmah, Yunita Padilah, Tesa Nur Rahmat Alfanza Rahmawati, Erika Rahmayani, Fenny Ratna Mufidah Reynaldi Azhar Ridha, Azhari Ali Ridwan, Muhammad Hafizh Rini Mayasari Rizal Fadilah Sani Shalihamidiq Senjaya, Rico Silvana Fitria Mandang Sisca Yuliantina Sisca Yuliatina Sita Alden Sofi Defiyanti Suna Mulyani Susilawati Susilawati Syafrida Nabila Salsabila Tesa Nur Padilah Tesa Nur Padilah Topan Adib Amrulloh, Ibnu Vania, Putri Wira Buana Yanti, Fajri

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