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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 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 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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Prediksi Penjualan Handphone di Toko X menggunakan Algoritma Regresi Linear Yubi Aqsho Ramadhan; Ahmad Faqih; Gifthera Dwilestari
Jurnal Informatika Terpadu Vol 9 No 1 (2023): Maret, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v9i1.692

Abstract

Mobile phones or smartphones have become a basic necessity in life. This device has transformed how we communicate and access information, and entertainment, making our lives easier and more comfortable. Store X sells mobile phones from various brands. The purpose of this research is to predict the sales of handphones in the next three months based on the sales data of brand X. Linear regression is used as the prediction method, with the number of handphones sold as the Y variable and the sales period as the X variable. RMSE (Root Mean Squared Error) and Relative Error are used to evaluate the prediction results. The predicted sales for the Entry category in the first month are 84 units, 86 units in the second month, and 88 units in the third month, while for the Mid category, 28 teams are sold in the first month, 29 teams in the second month, and 30 units in the third month. The RMSE evaluation result for the Entry category is 10.36, while the Relative Error value is 19.11%, and the RMSE value for the Mid category is 7.50, while the Relative Error value is 32.97%. The prediction of handphone sales using this linear regression method can be classified as sufficient or usable.
Analisis Sentimen Data Twitter Tentang Ekonomi Sirkular Menggunakan Algoritma Neural Network Berbasis Particle Swarm Optimization Fatihanursari Dikananda; Ahmad Rifa'i; Gifthera Dwilestari
Jurnal ICT: Information Communication & Technology Vol. 22 No. 2 (2022): JICT-IKMI, December 2022
Publisher : LPPM STMIK IKMI Cirebon

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

Abstract

The circular economy is a renewable and resilient industrial system that "eliminates" the end product cycle by implementing new procedures and business models, using renewable energy and chemicals. Develop use-based products for waste elimination and minimization. Some activities in the circular economy in people's lives include innovative solutions in plastic waste management, supply chain management, and charcoal briquettes from dry leaves. One application of the principles of a circular economy, such as waste management, is to classify, manage and develop plastic waste into a circular economy of valuable plastic waste. This means that it can support the economic life of the community. This study aimed to find out public opinion regarding the circular economy, which was conveyed through social media Twitter. Based on the reviews and public opinion about the circular economy that was shared through the Twitter media, sentiment analysis was conducted by classifying these opinions into positive, negative, and neutral reviews. The method used in this research is a machine learning technique with a neural network algorithm based on particle swarm optimization (PSO). The results of this research on Twitter data sentiment analysis on circular economy obtained a population size of 4 for particle swarm optimization parameters so that the accuracy rate reaches 75%. Using the neural network+PSO algorithm, while using the neural network algorithm alone, it gets an accuracy rate of 71.67%.
KLASIFIKASI PENERIMA BANTUAN SOSIAL DENGAN ALGORITMA RANDOM FOREST UNTUK PENANGANAN COVID 19 Abdur Rosid; Odi Nurdiawan; Gifthera Dwilestari
JURSIMA (Jurnal Sistem Informasi dan Manajemen) 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; Odi Nurdiawan; Gifthera Dwilestari
JURSIMA (Jurnal Sistem Informasi dan Manajemen) 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%. .
ANALISIS SENTIMEN PENGGUNA TWITTER TERHADAP PELAKSANAAN KURIKULUM MBKM Nur Amalia; Tati Suprapti; Gifthera Dwilestari
E-Link: Jurnal Teknik Elektro dan Informatika Vol 18 No 1 (2023): Mei 2023
Publisher : Universitas Muhammadiyah Gresik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30587/e-link.v18i1.5335

Abstract

Kurikulum Merdeka Belajar Kampus Merdeka (MBKM) merupakan suatu kebijakan yang dicetuskan oleh Menteri Pendidikan dan Kebudayaan Indonesia. Isi mengenai kebijakan tersebut diantaranya mempermudah perguruan tinggi membuka prodi baru, kemudahan perguruan tinggi negeri memiliki badan hukum, serta hak mahasiswa mendapatkan kebebasan dengan mengambil pembelajaran satu semester diluar program studi dan dua semester diluar kampus. Adapun program yang menunjang mahasiswa belajar diluar kampus ialah magang, studi independen, wirausaha, KKN tematik/membangun desa, program kemanusiaan, pertukaran pelajar, riset, dan asistensi mengajar. Namun, dalam pelaksanaan kurikulum MBKM ini tidak luput dari berbagai hambatan baik dalam segi pemahaman, kesiapan perguruan tinggi, maupun mahasiswa itu sendiri. Hal ini menimbulkan berbagai opini masyarakat baik yang bersifat positive maupun negative terhadap pelaksanaan kurikulum MBKM yang di tuangkan melalui media sosial twitter sehingga perlu adanya analisa untuk mendapatkan informasi melalui tanggapan tersebut. Tujuan penelitian ini untuk menganalisis sentimen pengguna twitter terhadap pelaksanaan kurikulum MBKM untuk mengelompokkan tanggapan yang bersifat positive dan negative dari tulisan menggunakan analisa teks. Metode pada penelitian menerapkan Naïve Bayes dan Decision Tree untuk melihat akurasi dari kedua algoritma tersebut. Dataset berupa tanggapan pengguna twitter terhadap pelaksanaan kurikulum MBKM, kemudian dilakukan pengelompokkan sentimen pada data tersebut untuk diklasifikasi. Dataset yang digunakan sebanyak 1275 tweet. Hasil dari penelitian ini menunjukan algoritma Naive Bayes cukup baik dengan nilai akurasi sebesar 81,15%, recall 75,98%, dan precision 91,10%. Sedangkan Decision Tree memiliki nilai akurasi 68,19%, recall 96,33% dan precision sebesar 37,83%.
KLASIFIKASI PENERIMA BANTUAN SOSIAL DENGAN ALGORITMA RANDOM FOREST UNTUK PENANGANAN COVID 19 Abdur Rosid; Odi Nurdiawan; Gifthera Dwilestari
JURSIMA (Jurnal Sistem Informasi dan Manajemen) 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; Odi Nurdiawan; Gifthera Dwilestari
JURSIMA (Jurnal Sistem Informasi dan Manajemen) 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 Arif Rinaldi Dikananda; Saefullah Fasa; Irfan Ali; Gifthera Dwilestari
JURSIMA (Jurnal Sistem Informasi dan Manajemen) Vol 10 No 3: 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 Irvan Himawan; Odi Nurdiawan; Gifthera Dwilestari
JURSIMA (Jurnal Sistem Informasi dan Manajemen) Vol 10 No 3: 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.
Penerapan Metode Support Vector Machine Pada Sentimen Analisis Pengguna Twitter Terhadap Konser K-Pop Dessy Angelina; Umi Hayati; Gifthera Dwilestari
KOPERTIP : Scientific Journal of Informatics Management and Computer Vol. 7 No. 1 (2023): KOPERTIP : Jurnal Ilmiah Manajemen Informatika dan Komputer
Publisher : Puslitbang Kopertip Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32485/kopertip.v7i1.251

Abstract

K-pop atau Korean Pop saat ini sangat terkenal eksis di kalangan remaja dengan banyaknya penggemar di seluruh mancanegara. Dalam laporan Twitter 2022, Indonesia menjadi Negara dengan jumlah penggemar kpopers terbesar di seluruh dunia. Twitter menjadi media sosial masa kini untuk saling berbagi informasi secara update ataupun berbagi tuitan. Twitter kerap digunakan Kpopers untuk berinteraksi dengan penggemar lainnya ataupun mencari informasi terbaru tentang idola mereka. Tak jarang antar pengguna Tewitter sebagai fans K-pop saling beradu argumen untuk melindungi atau membela citra idolanya masing-masing dari komentar buruk atau tuduhan. Salah satu fans K-pop yang belakangan ini sedang ramai diperbincangkan kpopers dan non fans di media sosial Twitter yaitu fans k-pop Boy Group dari agensi SM Entertaiment bernama NCT 127 (Neo Culture Technology), yang pada tanggal 4 November 2022 lalu menggelar konser tour dunianya di Jakarta. Konser hari pertama mengalami kericuhan sehingga konser diberhentikan sebelum waktunya oleh kepolisian, karena ada lebih dari 30 orang fans yang pingsan dan kesusahan bernafas atas aksi saling dorong mendorong yang terjadi oleh para NCTZen (Fans NCT) untuk berebut signed bola dari para member NCT 127. Dari kejadian kericuhan konser NCT 127 The Link : Jakarta day 1 yang berlokasi di ICE BSD Tangerang tersebut mengundang banyak perhatian pengguna Twitter dari sesama NCTZen, fans lain ataupun non fans di media sosial dengan berbagai tuitan yang bersifat negatif dan positif. Penelitian ini bertujuan untuk mengelompokan sentimen menggunakan metode Klasifikasi Support Vector Machine (SVM) dengan mengetahui pola permasalahan sehingga dapat menghasilkan nilai-nilai yang dikelompokan berupa sikap pengguna Twitter terhadap Konser K-Pop dengan dua nilai yakni lebih condong nilai positif atau nilai negatif. Dari hasil klasifikasi mengguakan Support Vector Machine (SVM) dengan dataset sebanyak 841, diperoleh akurasi 76,64% dengan nilai positif sebanyak 587 dan nilai negatif sebanyak 368. Dengan demikian hasil penelitain ini membuktikan bahwa pengguna Twitter dalam menanggapi permasalahan tentang konser K-pop cenderung lebih banyak berkomentar positif sesuai dengan dataset yang telah penulis ambil sebagai bahan pengujian dan dengan menggunakan metode Support Vector Machine (SVM) dalam hal ini cukup baik untuk mengklasifikasikan dataset berupa teks.
Co-Authors Abdul Ajiz Abdul Ajiz, Abdul Abdul Rauf Chaerudin Abdullah Syafii Abdullah Syafii Aby Febrian Ade Irma Purnamasari Ade Irma Purnamasari Ade Kurnia, Dian 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 Amal Rois, Moh. Ichlasul Ananda Rafly Andi Suandi Anita Nur Kirana Anwar Musaddad Apriliyani, Ela Arif Rinaldi Dikananda Arifin, Bagas Adam Athhar Hafizha Luthfi Auliya Azmi Afifah, Turfa 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 Fajar Maulana Adji, Moh 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 Irfan Ali Irfan Ali Irfan Ali, Irfan Irma Agustina Irma Purnamasari, Ade Irvan Himawan Jayawarsa, A.A. Ketut Karimah, Ayu Kaslani Kencana, Junaedi Surya 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 Muzaki, Fazri 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 NURUL HIDAYAH Nurwahidah, Dalilah Odi Nurdiawan Odi Nurdiawan Permana, Sandy Eka Pratama, Denni Prihartono, Willy Puspita Maulana Arumsari R, Nining Raditya Danar Dana Raena Agustin Laeliyah Rahaditya Dasuki Ramdhan, Dadan Ramiro Firjatullah, Federicko Ranu Husna Riyana, Iis Rizki Fauzi, Ahmad Rizqy, Muhammad Enricco Rosmeri Manurung, Agnes Rudi Kurniawan Saeful Anwar Saeful, Agung Saefullah Fasa Saepu Qirom, Dani Saepudin, Asep Saepul Hadi Sagita, Ayu Salsabila, Putri Septiana, Angga 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 Wulan Suci, Salwa Yubi Aqsho Ramadhan