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All Journal Jurnal technoscientia Jurnal Informatika dan Teknik Elektro Terapan Information System for Educators and Professionals : Journal of Information System Informatics for Educators and Professional : Journal of Informatics Information Management For Educators And Professionals (IMBI) JITK (Jurnal Ilmu Pengetahuan dan Komputer) KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer JURNAL ILMIAH INFORMATIKA Pelita : Jurnal Penelitian dan Karya Ilmiah JURIKOM (Jurnal Riset Komputer) Jurnal Informasi dan Komputer JOURNAL INFORMATICS, SCIENCE & TECHNOLOGY Jurnal Tekno Kompak Jurnal ICT : Information Communication & Technology Jurnal Manajemen Komunikasi Jurnal Informatika dan Rekayasa Perangkat Lunak JURSIMA (Jurnal Sistem Informasi dan Manajemen) JATI (Jurnal Mahasiswa Teknik Informatika) E-Link: Jurnal Teknik Elektro dan Informatika Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) MEANS (Media Informasi Analisa dan Sistem) JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Informatika Terpadu Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Informatika dan Teknologi Informasi INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi JURSIMA Jurnal Teknologi Ilmu Komputer AMMA : Jurnal Pengabdian Masyarakat Jurnal Informatika Polinema (JIP) Jurnal Sistem Informasi dan Manajemen Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Ilmiah Sistem Informasi Informasi interaktif : jurnal informatika dan teknologi informasi
Gifthera Dwilestari
STMIK IKMI Cirebon
Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Social Sciences Other

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ANALYSIS STUDENT EMOTIONS AND MENTAL HEALTH ON CUMULATIVE GPA USING MACHINE LEARNING AND SMOTE Fadhil Muhammad Basysyar; Gifthera Dwilestari; Ade Irma Purnamasari
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5967

Abstract

This research investigates the impact of emotions and mental health on students' cumulative grade point average (CGPA) using machine learning classification algorithms while addressing data imbalances with the Synthetic Minority Oversampling Technique (SMOTE). Emotional well-being and mental health are acknowledged as vital determinants of academic achievement. Data imbalance, particularly in mental health metrics such as anxiety and depression, frequently compromises forecast accuracy. This study improves the accuracy of CGPA prediction based on emotional and mental health factors by utilizing SMOTE in machine learning models such as logistic regression and random forest. A dataset including 226 university students, including academic records and self-reported mental health evaluations, was evaluated. The random forest model attained an accuracy of 87.63%, exceeding the logistic regression model's accuracy of 86.56%. These findings emphasize the significant role of emotions and mental health in academic outcomes and validate SMOTE’s efficacy in addressing class imbalance. This work offers a fresh technique in educational data mining by revealing the possibility for improved academic achievement forecasts based on psychological characteristics, helping to the development of targeted therapies for students experiencing emotional issues. Implications for educational policy emphasize the significance of mental health support systems in promoting academic achievement. Subsequent research should investigate supplementary psychological variables and comprehensible models to improve predictive accuracy and facilitate evidence-based policymaking.
IMPLEMENTASI AKURASI MODEL NAIVE BAYES MENGGUNAKAN SMOTE DALAM ANALISIS SENTIMEN PENGGUNA APLIKASI BRIMO Hermawan, Muhammad Andi; Faqih, Ahmad; Dwilestari, Gifthera
Jurnal Informatika dan Teknik Elektro Terapan Vol 13, No 1 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i1.5748

Abstract

Aplikasi Brimo dari Bank Rakyat Indonesia (BRI) menjadi salah satu platform utama interaksi nasabah dengan layanan perbankan. Analisis sentimen ulasan pengguna aplikasi ini pentring untuk memahami pendapat dan menaikkan kualitas pelayanan. Penelitian ini menggunakan algoritma Naïve bayes dengan menerapkan model Smote (Synthetic Minority Over-sampling Technique) untuk menangani ketidakseimbangan kelas antara kelas positif dan negatif dalam data ulasan pengguna. Dataset yang di peroleh mencapai 1.000 ulasan Play store yang di proses melalui tahap pengumpulan, pra-pemrosesan teks, dan evaluasi menggunakan Confusion Matrix. penelitian menunjukkan bahwa metode SMOTE secara signifikan meningkatkan kinerja model, dengan recall untuk sentimen negatif meningkat dari 0,55 menjadi 0,87 dan F1-score dari 0,71 menjadi 0,84. Akurasi model juga naik dari 93% menjadi 95%, dengan pengurangan False Negatives. Temuan ini membuktikan efektivitas SMOTE dalam meningkatkan akurasi dan representasi model untuk memahami opini pengguna BRImo secara lebih baik.
IMPLEMENTASI APLIKASI PEMBELAJARAN PETUALANGAN BERBASIS AUGMENTED REALITY UNTUK MENINGKATKAN INTERAKTIVITAS DI SMAN 1 DUKUPUNTANG Hermawan, Bagus; Faqih, Ahmad; Dwilestari, Gifthera
Jurnal Informatika dan Teknik Elektro Terapan Vol 13, No 1 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i1.5688

Abstract

 Kemajuan teknologi telah membawa perubahan signifikan dalam pendidikan, termasuk di SMAN 1 Dukupuntang, Kabupaten Cirebon, yang menghadapi masalah kejenuhan belajar. Penelitian ini bertujuan merancang aplikasi pembelajaran berbasis Augmented Reality (AR) untuk meningkatkan motivasi dan pemahaman siswa dalam Biologi, Fisika, Kimia, dan Matematika. Dengan menggunakan pendekatan Research and Development (R&D) dan model ADDIE (Analyze, Design, Development, Implementation, Evaluation), aplikasi AR ini berhasil mengurangi kejenuhan dan meningkatkan keterlibatan siswa. Pada tahap analisis, observasi dan wawancara menunjukkan kebutuhan siswa akan metode belajar yang lebih menarik. Desain aplikasi mencakup skenario petualangan dengan visualisasi 3D, mempermudah pemahaman konsep abstrak seperti struktur atom. Implementasi di kelas menggunakan marker-based tracking untuk meningkatkan partisipasi siswa. Evaluasi menunjukkan peningkatan minat belajar dari 61% menjadi 90% setelah menggunakan aplikasi. Umpan balik siswa dan guru menyatakan aplikasi ini memenuhi kebutuhan media belajar inovatif, meskipun ada tantangan perangkat dan koneksi internet. Hasil penelitian ini menekankan bahwa AR efektif meningkatkan interaktivitas pembelajaran, menciptakan pengalaman belajar menyenangkan dan relevan, serta memiliki potensi besar untuk diterapkan lebih luas guna mendukung proses belajar yang inovatif di sekolah.
Bibliometric Analysis Impact of Machine Learning on Mental Health in Student Learning Fadhil Muhammad Basysyar; Dadang Sudrajat; Gifthera Dwilestari
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The integration of machine learning in educational settings offers promising avenues for addressing mental health challenges among students [1]. This study conducts a bibliometric analysis to explore the impact of machine learning on mental health within student learning environments. By systematically reviewing peer-reviewed articles, conference papers, and relevant literature from the past decade, this research identifies key trends, challenges, and opportunities in this emerging field. The study focuses on the effectiveness of different machine learning methodologies in detecting, diagnosing, and intervening in mental health issues, highlighting the potential for early identification and personalized support. Furthermore, it addresses critical concerns related to data privacy, ethical considerations, and algorithmic biases, which are paramount for the responsible deployment of these technologies. The findings reveal significant advancements in the application of natural language processing and wearable technology data for mental health monitoring. However, gaps remain in longitudinal studies and the consideration of cultural and contextual factors. This research contributes to the existing body of knowledge by providing a comprehensive overview and identifying directions for future research, ultimately aiming to enhance the well-being and academic performance of students through innovative machine learning solutions.
Systematic Bibliometric Research Trend of Text Mining on Product Comments in Business Ecosystem Gifthera Dwilestari; Fadhil Muhammad Basysyar
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The business ecosystem represents a new paradigm that has gained considerable attention among researchers and practitioners. Despite its popularity, systematic literature reviews utilizing bibliometric analysis within this context remain sparse. This study aims to conduct a comprehensive bibliometric and visualization analysis of business ecosystem research, focusing on the impact of text mining on product comments. Employing VOSviewer for visualization, the study evaluates 95 scientific articles indexed in Scopus quartiles Q1 to Q4 from the Scopus database over the last decade (2001-2024). The bibliometric analysis identifies the most productive publishers, the evolution of scientific articles, and citation patterns. Visualization with VOSviewer reveals prevalent terms in titles and abstracts, author collaboration networks, and assists in identifying novel and underexplored topics within the business ecosystem. The findings provide valuable insights for researchers and practitioners, highlighting key trends and potential research gaps, thus contributing to the advancement of knowledge in the field.
PENGELOMPOKAN INDEKS PRESTASI KUMULATIF MAHASISWA BERDASARKAN EMOSI MENTAL MENGGUNAKAN K-MEANS CLUSTERING Muhammad Basysyar, Fadhil; Dwilestari, Gifthera
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 6 (2024): JATI Vol. 8 No. 6
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i6.11597

Abstract

Kinerja akademik mahasiswa, yang diukur melalui Indeks Prestasi Kumulatif (IPK), dipengaruhi oleh berbagai faktor, termasuk kondisi emosi mental seperti stres, kecemasan, dan depresi. Penelitian ini bertujuan untuk mengelompokkan mahasiswa berdasarkan IPK dan kondisi emosi mental mereka menggunakan algoritma K-Means Clustering. Dengan menggunakan dataset yang terdiri dari 226 mahasiswa, yang mencakup variabel IPK dan status emosi mental (depresi, kecemasan, serangan panik), dilakukan analisis pengelompokan untuk mengidentifikasi hubungan antara kondisi emosional dan performa akademik. Hasil penelitian menunjukkan bahwa mahasiswa terbagi menjadi dua klaster utama, di mana klaster pertama terdiri dari mahasiswa dengan kondisi emosional stabil dan IPK yang tinggi, sementara klaster kedua mencakup mahasiswa dengan kondisi emosi yang lebih rentan, meskipun masih memiliki IPK yang tinggi. Hasil analisis menunjukkan pembentukan dua klaster utama, yaitu klaster 0 dan klaster 1, yang merepresentasikan variabilitas signifikan dalam IPK, tingkat depresi, kecemasan, serangan panik, dan kebutuhan akan perawatan spesialis. Evaluasi dilakukan menggunakan Davies Bouldin Index untuk mengukur kualitas kluster yang terbentuk yaitu 2 klaster dengan nilai Dbi sebesar 1.12.
KLASIFIKASI PENERIMA BANTUAN SOSIAL DENGAN ALGORITMA RANDOM FOREST UNTUK PENANGANAN COVID 19 Rosid, Abdur; Nurdiawan, Odi; Dwilestari, Gifthera
JURSIMA Vol 10 No 2 (2022): Jursima Vol. 10 No. 2, Agustus Tahun 2022
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i2.398

Abstract

The Covid 19 outbreak has an impact on the community so that there are family heads who cannot work in general. The policy pursued by the central government is to provide assistance to workers who have salaries below 5 million and other programs. The obstacles faced to the community are not exactly recipients of assistance in accordance with the criteria set by the government. The criteria set by the government are workers who have salaries below 5 million. The purpose of the study can model the recipients of social assistance that is on target, so that the assistance can be useful in the time of the Covid 19 pandemic. This method of approaching research uses knowladge data discovery with the first stage of data obtained by social services in 2020 the second stage of data classification based on the riteri that has been established. The third stage of preprocessing is used to clean up noise data, stage four of the random forest model by using rapid miner tool version 9.9. Stage six discussion of the results of the model produced from random forest. The results expected in the study get a good model so that it becomes a recommendation in determining the recipients of sosial assistance
PENERAPAN MACHINE LEARNING UNTUK MENENTUKAN KELAYAKAN KREDIT MENGGUNAKAN METODE SUPPORT VEKTOR MACHINE Syafi'i, Syafi'i; Nurdiawan, Odi; Dwilestari, Gifthera
JURSIMA Vol 10 No 2 (2022): Jursima Vol. 10 No. 2, Agustus Tahun 2022
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i2.422

Abstract

Credit is one of the services provided by banks, credit risk that occurs in the provision of credit loans, in the case that the customer is unable to pay the loan received is always considered by the bank, and supervises the customer to reduce risk. The main risk for banks and financial institutions is to differentiate creditors who have the potential for bad loans, this crisis is a concern for financial institutions about credit risk. SUPPORT VEKTOR MACHINE algorithm is an algorithm used to form a decision tree. The decision tree is a very powerful and well-known classification and prediction method. The richer the information or knowledge contained by the training data, the accuracy of the decision tree will increase. The SUPPORT VEKTOR MACHINE algorithm classification method can determine the credit worthiness of the national civil capital capitals as evidenced by the performance table data consisting of the AUC results, Acuracy results. The results of the application of machine learning using the vector machine support algorithm against cooperative data in KPRI "RUKUN" SMKN 1 Lemahabang to determine creditworthiness based on the results of the Performance Vector from the Support Vector Machine algorithm resulted in smooth prediction, smooth true 130, prediction of jammed, true jam 72, current prediction true jam 41, prediction of jammed true jam 332. The accuracy rate of the performance vector of the support vector algorithm is 80.34%. .
RANCANG BANGUN APLIKASI SISTEM INFORMASI PENDATAAN PELAUT BERBASIS WEB Dikananda, Arif Rinaldi; Fasa, Saefullah; Ali, Irfan; Dwilestari, Gifthera
JURSIMA Vol 10 No 3 (2022): Jursima Vol.10 No.3
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i3.473

Abstract

PT. Abdi Marine is one of the companies that has not used a web-based information system in the marine data collection section, where the data processing system is still manual. It often happens that seafarers' registration and flight date research takes up a lot of paper and seafarer data storage space, the calculation of the date is less accurate and making reports of incoming and outgoing seafarers' data takes a lot of time. To emphasize and learn in understanding the problems as described, the problem formulation that researchers can explain is to design a computerized marine crew data collection information system, create a database of data services for managers to carry out their work. The purpose of this research is to find out, develop and create an ongoing data collection application system into the PHP and HTML programming language using the MySQL database. So that researchers can draw conclusions in processing sailor crew data collection by implementing applications that have been designed and built in a systematic and structured manner, so that the level of damage in the process of implementing sailor crew data collection can be resolved.
Irvan Himawan PREDIKSI HARGA SAHAM DENGAN ALGORITMA REGRESI LINIER DENGAN RAPIDMINER Himawan, Irvan; Nurdiawan, Odi; Dwilestari, Gifthera
JURSIMA Vol 10 No 3 (2022): Jursima Vol.10 No.3
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i3.475

Abstract

Stock investment in the capital market is very important for every company in the world. Stock prices in the capital market move very randomly, the highs and lows of stock prices are influenced by many factors. Therefore, it is necessary to predict the stock price so that it can help investors to see investment prospects in the future. In this study, the prediction of the stock price of BRI Bank with the BBRI stock code will be carried out, using an algorithm, namely Linear Regression on rapid miners. This Linear Regression Algorithm is the best algorithm to use because it is the most complex compared to other algorithms. Based on signaling theory, which are information signals needed by investors, the value of forecasting results that have been obtained can be used to consider investors' decisions that the stock has high or low risk in the future. Based on the theory of risk, this forecasting analysis helps investors to minimize losses. Stock prediction is one of the technical analysis. Stock buying and selling transactions without technicalities are gambling behavior and contain gharar or ambiguity. The impact of not using this technical analysis clearly resulted in transactions containing maisir and gharar which were clearly prohibited. The historical stock data used in the test was obtained from the finance.yahoo.com web page with the category PT. Bank Rakyat Indonesia Tbk, or with the issuer code BBRI shares. What will be used is annual data for the last 5 years in the form of time series accompanied by open, high, low and volume variables as independent variables and close as dependent variables. The algorithm used is multiple linear regression.
Co-Authors Abdul Ajiz Abdul Ajiz, Abdul Abdul Rauf Chaerudin Abdullah Syafii Abdullah Syafii Aby Febrian Ade Irma Purnamasari Ade Irma Purnamasari Ade Rizki Rinaldi Agis Maulana Robani Agung Nugraha agus bahtiar Ahmad Faqih Ahmad Faqih Ahmad Rifa'i Ahmad Zam Zami Aldiani, Dea Alia Cahyani, Cica Alibasyah, Aziz Ananda Rafly Andi Suandi Anita Nur Kirana Anwar Musaddad Apriliyani, Ela Arif Rinaldi Dikananda Arifin, Bagas Adam Athhar Hafizha Luthfi Auliya Bagas Al Haddad Bambang Siswoyo Basysyar, Fadhil Muhammad Caswadi, Caswadi Chaerudin, Chaerudin Cindyk Irawanto Dadang Sudrajat Dea Miftahul Huda Dessy Angelina Destriyanah, Riska Dian Ade Kurnia Dias Bayu Saputra Dienwati Nuris, Nisa Dienwati, Nisa Dikananda, Arif Rinaldi Dikananda, Fatihanursari Dzaffa 'Ulhaq Edi Tohidi Edi Tohidi Eka Permana, Sandy Fadhil Muhammad Basysyar Fadhil Muhammad Basysyar Fajar Fauzan, Muhammad Fajria, Azzahra Moudy Fasa, Saefullah Fathurrohman Fathurrohman Fatihanursari Dikananda Faujia, Agnes Fithrah Ali, Dini Salmiyah Fuadi Ahmad, Cecep Hamonangan, Ryan Haris Abdul Hadi Herdiana, Rulli Hermawan, Bagus Hermawan, Muhammad Andi Hilya Ashfia Nabila Himawan, Irvan Hira Wahyuni Azizah Hoeriah, Dede Hoerunnisa, Anis Iin Iin Solihin Iis Riyana Irfan Ali Irfan Ali Irfan Ali, Irfan Irma Agustina Irma Purnamasari, Ade Irvan Himawan Jayawarsa, A.A. Ketut Karimah, Ayu Kaslani Kencana, Junaedi Surya Khaerul Anam Khoirul Huda, Muhammad Kokom Komariyah Lestari, Anjar Ayuning Martanto . Mar’atun Sholihah, Oliffia Maulana Sidiq, Cecep Mochamad Aditya Sunaryo Muhammad Abdurohman Muhammad Basysyar, Fadhil Mulyawan Mulyawan, Mulyawan Musliyadi, Mar'i Nana Suarna Nana Suarna Nana Suarna Narasati, Riri Narasati Nining R Nining Rahaningsih Nisa Dieanwati Nuris Nur Amalia Nur Kirana, Anita Nuraini, Asyifa Nurhakim, Bani Nurul Aini, Yuli Nurwahidah, Dalilah Odi Nurdiawan Odi Nurdiawan Permana, Sandy Eka Pratama, Denni Prihartono, Willy Puji Pramudya Marta Purnamasari, Ade Irma Purnamasari, Adinda Puspita Maulana Arumsari R, Nining Raditya Danar Dana Raena Agustin Laeliyah Rahaditya Dasuki Ramdhan, Dadan Ramiro Firjatullah, Federicko Ranu Husna Rini Astuti Rizaldy, Farhan Rizqy, Muhammad Enricco Rohmat, Cep Lukman Rosmeri Manurung, Agnes Rudi Kurniawan Saeful Anwar Saeful, Agung Saefullah Fasa Saepu Qirom, Dani Saepudin, Asep Saepul Hadi Sagita, Ayu Salsabila, Putri Sandy Eka Permana Septiana, Angga Setiawan, Riyan Sri Suwartini Suandi, Andi Suarna, Nana Subhiyanto, Fajar Sunana, Heliyanti Suryani Dewi, Ike Susana, Heliyanti Syafi'i Syafi'i Syafi'i, Syafi'i Tati Suprapti Tohidi, Edi Tuti Hartati Umi Hayati Vibrianti, Vera Wahyudin, Edi Yubi Aqsho Ramadhan Zacky Muhammad Dinata