Article Per Year (5 Year)
p-Index From 2021 - 2026
8.521
P-Index
This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) SITEKIN: Jurnal Sains, Teknologi dan Industri JUSIFO : Jurnal Sistem Informasi Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Sistemasi: Jurnal Sistem Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Inotera JURIKOM (Jurnal Riset Komputer) Jurnal Sistem Cerdas Building of Informatics, Technology and Science Jurnal Sistem Informasi dan Informatika (SIMIKA) Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Indonesian Journal of Data and Science Jurnal Teknik Informatika (JUTIF) KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Indonesian Journal of Informatic Research and Software Engineering Malcom: Indonesian Journal of Machine Learning and Computer Science CONSEN: Indonesian Journal of Community Services and Engagement Journal of Informatics, Electrical and Electronics Engineering The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika Jurnal Ilmu Komputer dan Teknik Informatika Jurnal Pengabdian Masyarakat Berdampak
Inggih Permana
Universitas Islam Negeri Sultan Syarif Kasim Riau
Religion Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

Published : 92 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 92 Documents
Search

 3   4   5   6   7 
Reduksi Jumlah Aturan Penentuan Kata Tunjuk dalam Bahasa Arab Menggunakan Algoritma ID3 Permana, Inggih; Febi Nur Salisah, Febi Nur Salisah
Indonesian Journal of Informatic Research and Software Engineering (IJIRSE) Vol. 1 No. 2 (2021): Indonesian Journal of Informatic Research and Software Engineering
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (839.751 KB) | DOI: 10.57152/ijirse.v1i2.144

Abstract

Bahasa Arab memiliki tidak kurang dari sepuluh bentuk kata tunjuk yang bisa digunakan. Untuk menentukan kata tunjuk tersebut harus memperhatikan empat variabel pada objek yang ditunjuk. Dari keempat variabel tersebut menghasilkan 24 kondisi (aturan) yang mungkin muncul untuk menentukan kata tunjuk. Banyaknya aturan yang mungkin muncul tersebut menjadi salah satu sebab pelajar Bahasa Arab melakukan kesalahan menggunakan kata tunjuk. Berdasarkan permasalahan tersebut, maka penelitian ini telah mereduksi aturan tersebut dengan menggunakan Algoritma ID3. Pohon keputusan hasil Algoritma ID3 berhasil mereduksi aturan sebanyak 50%. Selain itu, berdasarkan pohon keputusan tersebut dapat disimpulkan bahwa untuk menentukan kata tunjuk cukup memperhatikan tiga variabel saja.
Pengaruh Normalisasi Data Terhadap Performa Hasil Klasifikasi Algoritma Backpropagation: The Effect of Data Normalization on the Performance of the Classification Results of the Backpropagation Algorithm Permana, Inggih; Salisah, Febi Nur Salisah
Indonesian Journal of Informatic Research and Software Engineering (IJIRSE) Vol. 2 No. 1 (2022): Indonesian Journal of Informatic Research and Software Engineering
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (682.262 KB) | DOI: 10.57152/ijirse.v2i1.311

Abstract

Keberhasilan Algoritma Backpropagation (BP) tergantung pada kualitas data. Sehingga, normalisasi data merupakan proses yang penting. Akan tetapi, beberapa penelitian juga ada yang tidak menggunakan normalisasi data. Oleh sebab itu, penelitian ini mengukur pengaruh normalisasi data terhadap performa hasil klasifikasi Algoritma Backpropagation. Agar diketahui apakah normalisasi benar-benar bisa meningkatkan performa hasil klasifikasi pada Algoritma BP. Penelitian ini menggunakan tiga metode normalisasi data, yaitu: MinMax Normalization; MaxAbs Normalization; dan Z-Score Normalization. Berdasarkan hasil percobaan didapat bahwa jika data yang digunakan terdapat perbedaan rentang nilai antar atribut yang tidak berbeda jauh, maka BP tanpa normalisasi data bisa menjadi pilihan terbaik. Akan tetapi jika pada data terdapat atribut yang memiliki perbedaan rentang nilai yang jauh dari atribut lainnya, maka menggunakan normalisasi data bisa menjadi pilihan terbaik. Berdasarkan hasil percobaan juga didapat bahwa Z-Score Normalization merupakan metode normalisasi terbaik.
Penerapan Metode Regresi Linier Untuk Prediksi Jumlah Orang Terlantar Di Provinsi Riau Windy Amelia Putri; M. Afdal; Permana, Inggih; Zarnelly
Jurnal Sistem Cerdas Vol. 6 No. 2 (2023)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v6i2.291

Abstract

Displaced people are residents who for some reason cannot meet their needs naturally, both spiritually, physically, and socially. The problem of displaced people occurs for various reasons and urbanization is one of them. Social Service is a government agency that plays a role in improving the quality of social welfare of individuals, groups, and communities. Many community services are carried out by Social Service and one of them is the repatriation of displaced persons. Based on data from the Riau Province Social Service, the number of displaced people in Riau Province from year to year has increased or decreased erratically. This is of course a problem that hinders the Riau Province Social Service in its internal processes such as determining strategies or policies to make decisions. Therefore, this research was conducted to overcome these problems. This research was conducted using the linear regression method with a MAPE result of 7.09% which will be implemented into a prediction application for the number of displaced people and aims to help the Riau Province Social Service get information on the number of displaced people in the next period. Based on the results of the Blackbox test, shows that all menus and features have run very well and obtained a User Acceptance Test (UAT) calculation value of 92%.
Klasifikasi Penerima Bantuan Beras Miskin Menggunakan Algoritma K-NN, NBC dan C4.5 Pristiawati, Andani Putri; Permana, Inggih; Zarnelly, Zarnelly; Muttakin, Fitriani
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3617

Abstract

One of the tasks of the Dumai City Social Service is to provide poor rice assistance to people in need. The problem that often occurs in the distribution of rice to the poor is that the target recipients of poor rice often occur. In overcoming the existing problems, this research has carried out classification models using the K-Nearest Neighbor (K-NN) algorithm, Naïve Bayes Classifier (NBC), and C4.5 Algorithm. Based on the experimental results, it was found that the best classification model was produced by the K-NN Algorithm with a value of K equal to 21. Besides that, the C4.5 algorithm succeeded in making a decision tree for the classification model with the lowest complexity because it succeeded in reducing the number of attributes from 33 to 5 attributes. The decision tree can be used as material for consideration to the Social Service in making decisions on Raskin beneficiaries.
Comparison of Classification Algorithm Performance for Diabetes Prediction Using Orange Data Mining Hafiz Aryan Siregar; Muhammad Zacky Raditya; Aditya Nugraha Yesa; Inggih Permana
Indonesian Journal of Data and Science Vol. 4 No. 3 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i3.103

Abstract

Diabetes is a disease that contributes to a relatively high mortality rate. The human death rate due to diabetes is a widespread issue globally. The primary goal of this research is to predict individuals suffering from diabetes using a publicly available dataset from the UCI Repository with the Diabetes Disease dataset. To obtain the best classification algorithm, a comparison is made among three algorithms: KNN, Naive Bayes, and Random Forest, commonly used for predicting diabetes. The comparison results indicate that the Random Forest algorithm is the appropriate and accurate algorithm for predicting individuals with diabetes, with an accuracy rate of 97%.
Peramalan Jumlah Kedatangan Wisatawan Menggunakan Support Vector Regression Berbasis Sliding Window Fitriah, Ma’idatul; Permana, Inggih; Salisah, Febi Nur; Munzir, Medyantiwi Rahmawita; Megawati, Megawati
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7408

Abstract

As a developing city, Pekanbaru has the potential for attractive tourist attractions for tourists. The arrival of tourists has had a big positive impact on the economy of Pekanbaru City. The number of tourist arrivals can experience ups and downs every month, for this reason it is necessary to forecast the number of tourists in the future. This research aims to apply the Orange Data Mining application in predicting the number of tourist arrivals by comparing the kernels in the Support Vector Regression (SVR) method and applying Sliding Window size 3 to window size 13 to transform into time series data. As well as sharing data using the K-Fold Validation method with a value of K-10. Then the performance of the kernels used can be seen using the Test and Score widget which presents the results of Root Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), dan R-squared(R2). The results for forecasting the number of tourist arrivals to Pekanbaru City using the SVR method show that the RBF Kernel is the optimal choice compared to the Polinomial and Linear Kernels. The results of the Test and Score widget show that the RBF Kernel with window size 10 has lower MAE, MSE and RMSE values, namely 0.118, 0.022 and 0.147. Apart from that, the comparison of R2 in window size 10 for Kernel RBF shows better performance with a value of 0.519.
Prediksi Jumlah Bayi Penerima Imunisasi DPT 1 dan DPT 2 Menggunakan Support Vector Regression Idriani R, Nova; Permana, Inggih; Salisah, Febi Nur; Megawati, Megawati; Rahmawita M, Medyantiwi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7694

Abstract

Vaccination against diphtheria, pertussis (whooping cough), and tetanus is known as DPT immunization, which protects a person from three serious diseases. This vaccine is given in the form of an injection where there are 5 antigens in one injection of the vaccine. DPT immunization is a complete routine immunization that will be continued in grades 1 to 6 elementary school. DPT immunization is feared by mothers because of the side effects that occur in babies after the vaccine injection, namely that the baby will have a fever and be fussy. This has resulted in delays in collecting data on babies who have received this immunization, which has an impact on estimates of babies who will receive DPT immunization in the following month. Of course, this will disrupt the stock of vaccines provided, causing the potential for them to be out of stock. To overcome this problem, it is necessary to collect data on babies who have received DPT in the previous month. This data will be used to predict babies who will receive DPT immunization in the following month using the Support Vector Regression (SVR) method. So that the community health center can provide information regarding the prediction of the number of babies who will receive DPT immunization. This method uses three kernels and a Sliding Window to divide the data into smaller segments, moving alternately across the time series data, making it suitable for predicting babies who will receive DPT immunization in the next time interval. From the three kernels used on the two data that have been separated into DPT 1 and DPT 2, windowing size 3 linear kernels were obtained which were selected as an accurate evaluation of model work on DPT 1 with MAPE values of 3.35, RMSE 0.193, and R2 0.1. And windowing size 3 RBF kernels are more optimal in DPT 2 with MAPE values of 7.86, RMSE 0.163, and R2 0.288.
Analisis Sentimen Masyarakat Mengenai Gerakan Childfree di Media Sosial X Menggunakan Algoritma NBC dan SVM: Sentiment Analysis of Childfree Campaign on X Social Media Using NBC and SVM Algorithms Putra, Moh Azlan Shah; Permana, Inggih; Afdal, M.
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 4 (2024): MALCOM October 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i4.1356

Abstract

Anak merupakan salah satu entitas yang umum dalam membentuk sebuah keluarga, namun dalam beberapa tahun kebelakang muncul pembahasan mengenai childfree. Dengan banyaknya perdebatan pro-kontra mengenai childfree, perlu dilakukannya sentimen analisis terkait isu ini. Penelitian ini bertujuan untuk menganalisis sentimen masyarakat mengenai gerakan childfree di media sosial X menggunakan algoritma Naïve Bayes Classifier (NBC) dan Support Vector Machine (SVM). Sentimen dibagi menjadi 3 kelas yaitu positif, negatif, dan netral. Penelitian ini mengumpulkan data dengan crawling data pada media sosial X dengan keyword childfree. Data yang diperoleh merupakan data teks mentah sehingga dibutuhkan tahap pra proses. Tahap pra proses yang dilakukan adalah tokenizing, case folding, filter stopword, stemming, TF-IDF, dan data balancing. Berdasarkan simulasi, performa algoritma NBC adalah: akurasi = 56,36%, presisi = 56,41%, dan recall = 56,35%, sedangkan performa algoritma SVM adalah: akurasi 76,12%, presisi 76,36%, dan recall 76,13%. Sehingga dapat disimpulkan bahwa SVM memiliki performa yang lebih baik dari pada NBC pada analisis sentimen di penelitian ini.
Prediksi Jumlah Pendaftar Jemaah Umrah Menggunakan Backpropagation dan Regresi Linear pada PT. Hajar Aswad Mubaroq: Prediction of the Registrant Umrah Congregation using Backpropagation and Linear Regression at PT. Hajar Aswad Mubaroq Fikri, M. Hayatul; Permana, Inggih; Mundzir, Mediantiwi Rahmawita; Megawati, Megawati
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 4 (2024): MALCOM October 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i4.1371

Abstract

Umrah adalah perjalanan menuju Baitullah (Ka'bah) di Makkah yang dilakukan untuk melaksanakan serangkaian amal ibadah dengan memenuhi persyaratan-persyaratan khusus. PT Hajar Aswad Mubaroq adalah salah satu agen perjalanan umrah yang secara konsisten siap memberikan layanan kepada calon Jemaah Umrah untuk melakukan ibadah di tanah suci. Pada saat ini PT. Hajar Aswad Mubaroq masih melakukan prediksi manual untuk menghitung prediksi jumlah Jemaah yang akan berangkat umrah. Salah satu akibat dari prediksi manual jumlah pendaftar Jemaah umrah dengan akurat adalah perselisihan jumlah booking pesawat yang terkadang terdapat kekurangan dan kelebihan pemesanan. Sehubungan dengan itu penelitian ini bertujuan memprediksi jumlah Jemaah Umrah PT. Hajar Aswad Mubaroq menggunakan Teknik Machine Learning Agar meminimalkan kesalahan dalam pemesanan penerbangan dan meningkatkan efisiensi analisis serta pengambilan kebijakan terkait data yang ada. Teknik Machine Learning yaitu metode Backpropagation dan Regresi Linear. Hasil penelitian menunjukkan performa terbaik untuk prediksi jumlah pendaftar Jemaah umrah PT. Hajar Aswad Mubaroq yaitu menggunakan algoritma Backpropagation dengan nilai RMSE sebesar 0.101 +/- 0.000, R2 sebesar 0.010 +/- 0.021 dan MAPE 19.74% pada percobaan window size 8.
Perbandingan Performa Algoritma RNN dan LSTM dalam Prediksi Jumlah Jamaah Umrah pada PT. Hajar Aswad: Comparison of RNN and LSTM Algorithm Performance in Predicting the Number of Umrah Pilgrims at PT. Hajar Aswad Al Kiramy, Razanul; Permana, Inggih; Marsal, Arif; Munzir, Medyantiwi Rahmawita; Megawati, Megawati
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 4 (2024): MALCOM October 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i4.1373

Abstract

Secara bahasa umrah bermakna ziarah atau berkunjung, sedangkan secara istilah umrah adalah perjalanan ke Baitullah di luar waktu haji dengan tujuan melaksanakan ibadah tertentu dan memenuhi syarat-syarat khusus. PT Hajar Aswad merupakan sebuah perusahaan travel umrah yang beroperasi di Indonesia. PT Hajar Aswad bertanggung jawab untuk mengatur perjalanan, akomodasi, transportasi, dan berbagai keperluan lainnya bagi para jemaah umrah, untuk itu perlu memiliki pemahaman yang baik mengenai pola dan tren jumlah jemaah umrah agar dapat mengoptimalkan operasional dan memberikan pelayanan yang memuaskan kepada jamaah. Oleh karena itu penelitian ini dilakukan untuk memprediksi jumlah jamaah umrah pada PT Hajar Aswad menggunakan algoritma RNN dan LSTM agar PT Hajar Aswad. . Hasil perbandingan kedua algoritma menunjukkan bahwa LSTM mampu memberikan hasil prediksi yang sedikit lebih baik dibandingkan RNN dengan parameter window size 7, optimizer Adam, batch size 8, dan learning rate 0,01. Model ini memiliki nilai RMSE sebesar 0,1758, MAPE sebesar 0,4846, dan R2 sebesar 0,5198.
Co-Authors Aditya Nugraha Yesa Agus Buono Ahsyar, Tengku Khairil Al Kiramy, Razanul Alfakhri, Rezky Alfaridzi, Gemma Tahmid Aliya, Rahma Andi Darlianto Andriyani, Dwi Ratna Anggi Widya Atma Nugraha Anggia Anfina Anisah Fitri Anjani, Yulia Merry Annisa Ramadhani Aprijon Arif Marsal Arif Marsal Arif Marsal Arifah Fadhila Andaranti Arifin, Abdullah Aufa Zahrani Putri Aulia Dina Bib Paruhum Silalahi Chinthia, Maulidania Mediawati Dedi Pramana Dessi Cahyanti Detha Yurisna Detha Yurisna Devi, Rahma Dzul Asfi Warraihan Eka Pandu Cynthia Eki Saputra Eki Saputra Endah Purnamasari Esis Srikanti Fadhilah Syafria Fadil Rahmat Andini Farahdina Risky Ramadani Febi Nur Salisah Febi Nur Salisah Fiki Fikri, M. Hayatul Fitriah, Ma’idatul Fitriah, Ma’idatul Fitriani Muttakin Fitriani Muttakin Fitriani Muttakin Gathot Hanyokro Kusuma Gurning, Umairah Rizkya Hafiz Aryan Siregar Hasbi Sidiq Arfajsyah Hendri, Desvita Hilda Mutiara Nasution Husaini, Fahri Idria Maita Idria Idriani R, Nova Ikhsani, Yulia Imam Muttaqin Intan, Sofia Fulvi Ismail Marzuki Jazma, Muhammad Jazman , Muhammad Jazman, Muhammad Kusuma, Gathot Hanyokro M Afdal M Afdal M Zaky Ramadhan Z M. Afdal M. Afdal M. Afdal M. Afdal M. Afdal Maulana, Rizki Azli Megawati Megawati - Mona Fronita, Mona Muhammad Afdal Muhammad Fikry Muhammad Jazman Muhammad Jazman Muhammad Jazman Muhammad Naufal, Muhammad Muhammad Zacky Raditya Mukmin Siregar Mundzir, Mediantiwi Rahmawita Munzir, Medyantiwi Rahmawita Mustakim Mustakim Mustakim Mustakim Mustakim Mustakim Mutia, Risma Muttakin, Fitriani Nabillah, Putri Nardialis Nardialis Nasution, Nur Shabrina Naufal Fikri, R. Adlian Negara, Benny Sukma Nesdi Evrilyan Rozanda Nesdi Evrilyan Rozanda Nisa', Sayyidatun Norhavina Norhavina Nunik Noviana Kurniawati Nurainun Nurainun Nuraisyah Nuraisyah Nurfadilla, Nadia Nurkholis Nurkholis nursalisah, febi Octavia, Sania Fitri Pratama, Arya Yendri Priady, Muhamad Ilham Pristiawati, Andani Putri Puput Iswandi Putra, Moh Azlan Shah Putra, Tandra Adiyatma Rahman, Eman Rahmawita M, Medyantiwi Rangga Arief Putra Rayean, Rival Valentino Restu Ramadhan Ria Agustina Rice Novita Rice Novita Rizka Fitri Yansi Rizki Pratama Putra Agri Rozanda, Nesdi Evrilyan Sabillah, Dian Ayu Salisah, Pebi Nur Sania Fitri Octavia Sanusi Shir Li Wang Siti Monalisa Sofia Fulvi Intan Susanti, Pingki Muliya Tasya Marzuqah Tengku Khairil Ahsyar Triningsih, Elsa Tshamaroh, Muthia Uci Indah Sari Ula, Walid Alma Vicky Salsadilla Wenda, Alex Wido Purnama Winda Wahyuti Windy Amelia Putri Wira Mulia, M. Roid Yusmar Yusmar Zarnelly Zarnelly Zarnelly Zarnelly Zarnelly Zarnelly Zarnelly