Article Per Year (5 Year)
p-Index From 2020 - 2025
3.092
P-Index
This Author published in this journals
All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) SITEKIN: Jurnal Sains, Teknologi dan Industri Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Informatika Universitas Pamulang Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Ekonomi Manajemen Sistem Informasi Jurnal Sistem informasi dan informatika (SIMIKA) Zonasi: Jurnal Sistem Informasi Journal of Applied Engineering and Technological Science (JAETS) Jurnal Computer Science and Information Technology (CoSciTech) KLIK: Kajian Ilmiah Informatika dan Komputer Malcom: Indonesian Journal of Machine Learning and Computer Science
Novriyanto Novriyanto
Jurusan Teknik Informatika, Fakultas Sains Dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Other

Published : 31 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 31 Documents
Search

 1   2   3   4 
Expert System for Diagnosing Student Depression using the Certainty Factor Method Elin Haerani (Scopus ID: 57191839502); Novriyanto Novriyanto; Agesta Putrama
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2021: SNTIKI 13
Publisher : UIN Sultan Syarif Kasim Riau

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

Abstract

KLASIFIKASI STATUS GIZI BAYI POSYANDU KECAMATAN BANGUN PURBA MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) Emir Ramon; Alwis Nazir; Novriyanto Novriyanto; Yusra Yusra; Lola Oktavia
Jurnal Sistem Informasi dan Informatika (Simika) Vol 5 No 2 (2022): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v5i2.2185

Abstract

This research was conducted to apply the Support Vector Machine algorithm in the process of classifying the nutritional status of infants under five. The nutritional status of early childhood can determine what kind of human resources as successors of a nation in the future. Good nutritional status plays an important role in determining the success or failure of efforts to increase human resources, so that data on the nutritional status of toddlers such as at the Posyandu, Bangun Purba District can be classified using Data Mining techniques using the Support Vector Machine algorithm. The results of this study using 80% of the data as training data and 20% of the data as training data are f1 score 0.865, accuracy 0.876, precision score 0.871, and recall score 0.876. The results showed that from a total of 347 data on the nutritional status of infants, there were 284 infants with good nutrition, 15 infants with poor nutrition, 23 infants with less nutrition, 8 infants with excess nutrition, 6 infants with obesity, and 11 infants at risk of overnutrition. Based on these results, there were 304 baby nutrition data that were classified correctly from a total of 347 baby data that were used as testing data. From this research, it can be concluded that the Support Vector Machine algorithm can classify infant nutrition data at the Posyandu, Bangun Purba District, well.
Implementasi Long Short Term Memory Neural Network Untuk Prediksi Indeks Harga Perdagangan Besar Hartini Hartini; Fitri Insani (Scopus ID: 57190404820); Novriyanto Novriyanto; Suwanto Sanjaya
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2022: SNTIKI 14
Publisher : UIN Sultan Syarif Kasim Riau

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

Abstract

Indeks Harga Perdagangan Besar (IHPB) merupakan indikator untuk menilai perkembangan perekonomian suatu negara. Penelitian IHPB bertujuan sebagai deflator Produk Domestik Bruto untuk perkembangan ekonomi. Penelitian ini dilaksanakan dengan studi kasus IHPB Indonesia, data sekunder yang diperoleh dari situs resmi Badan Pusat Statistik (BPS) pada bulan Januari 2000 sampai bulan November 2019. Metode yang digunakan untuk memprediksi adalah Long Short Term Memory. LSTM merupakan perkembangan Jaringan syaraf tiruan algoritma deep learning Recurrent Neural Network (RNN) yang dapat mengatasi salah satu kekurangan RNN yaitu kemampuan pengelolaan informasi dalam periode lama. Dalam penelitian ini LSTM berhasil memprediksi IHPB bulan berikutnya. Pengujian terbaik pada komoditas indeks umum tanpa impor dan ekspor migas memberikan hasil  MAPE 1,1437%, MSE 0,0002, RMSE 0,0135 dengan tingkat akurasi 98,8563%.
Pengelompokan pembagian zakat dengan menggunakan metode clustering k-means Alvin Alvin Anzaz Islami; Elin Haerani; Novriyanto; Alwis Nazir
Computer Science and Information Technology Vol 4 No 1 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i1.4804

Abstract

Zakat merupakan ibadah yang menyangkut harta benda. Zakat juga termasuk rukun islam yang ke empat yang memiliki tujuan menyucikan harta bagi setiap muslim dengan cara menyisihkan sebagian harta kekayaannya, jika telah mencapai waktu dan besaran jumlahnya diberikan kepada orang yang berhak menerimanya. Pengumpulan dan penyaluran zakat biasanya ditangani oleh Badan Amil Zakat (BAZ) yang ada disetiap wilayah Indonesia, salah satunya di Pekanbaru. Sesuai dengan peraturan ada dua tahap yang dilakukan dalam memberikan bantuan kepada para mustahik yaitu melakukan wawancara dan observasi lapangan, kemudian menentukan nominal bantuan yang diberikan dengan kategori Mustahik penerima bantuan zakat 1, zakat 2, dan zakat 3. Masalah yang sering dijumpai dalam penentuan calon penerima bantuan adalah cara dalam pemilihan Mustahik yang masih menggunakan cara manual, sehingga sering menimbulkan masalah seperti lamanya proses pemilihan dan terjadinya salah hitung sehingga hasil seleksi Mustahik menjadi kurang akurat. Untuk itu, perlu dibuat suatu analisis yang dapat mengolah data menjadi informasi. Data mining ialah proses untuk mengolah data menjadi suatu informasi dengan teknik statistik, AI, dan machine learning. Ada banyak metode dalam data mining. Pada penelitian ini menggunakan algoritma k-means clustering dan untuk pengujian menggunakan Davies Bouldin Index. berdasarkan pengujian menggunakan davies bouldin index (DBI) klaster 4 merupakan klaster terbaik dengan nilai 0.671, dimana jika nilainya semakin rendah maka akan semakin baik klaster tersebut
Classification Academic Data using Machine Learning for Decision Making Process Elin Haerani; Fadhilah Syafria; Fitra Lestari; Novriyanto Novriyanto; Ismail Marzuki
Journal of Applied Engineering and Technological Science (JAETS) Vol. 4 No. 2 (2023): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v4i2.1983

Abstract

One of the qualities of higher education is determined by the success rate of student learning. Assessment of student success rates is based on student graduation on time. Sultan Syarif Kasim State Islamic University Riau is one of the state universities in Riau, with a total of 30,000 students. Of all the active students, there are some who are not. Students who are not active in this case will affect the timeliness of their graduation. The university always evaluates the performance of its students to find out information related to the factors that cause students to become inactive so that they are more likely to drop out and what data affect students being able to graduate on time. The evaluation results are stored in an academic database so that the data can later be used as supporting data when making decisions by the university. This research used data science concepts to explore and extract data sets from databases to find models or patterns, as well as new insights that can be used as tools for decision-making. After the data was explored, machine learning concepts were used to identify and classify the data. The method used was the Decision Tree Method. The results of the study found that these two concepts can provide the expected results. Based on the test results, it is known that the attribute that influences the success of student studies is the grade point average (GPA), where the accuracy of the maximum recognition rate is 88.19%. Keywords : Data science; Decision Tree; Graduate on Time; Machine Learning;
Evaluasi Peforma Jaringan Internet Menggunakan Metode QoS Aditya Dyan Ramadhan; Iwan Iskandar; Novriyanto; Pizaini
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 6 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i6.892

Abstract

The use of the internet for learning activities for students aged 5-24 years is increasing rapidly from 33.98% in 2016 to 59.33% of students using the internet in 2020. Pekanbaru Labor Vocational School also uses internet network to support teaching and learning activities. The increasing performance in an internet network will increase the data traffic. The problem that arises is the length of time needed to access the internet so as to reduce learning productivity. QoS method is conducted in this research to analyze internet access speed and measure internet network performance in Pekanbaru Labor Vocational School. Based on the measurements taken, it can be seen that the throughput value obtained is an average throughput of 2191 Kbps, the delay value obtained is 5.70 ms, the packet loss value obtained is 1.3%, the jitter value obtained is 0.00854421ms. This value is the value of network speed measured using the QoS method by accessing several sites including www.youtube.com, www.detik.com and also the download speed. It can be concluded that referring to the TIPHON standard, the Quality of Service value obtained by SMK Labor Pekanbaru is classified as very good with an index of 4 and a percentage of 95-100%.
Analisis Kualitas Jaringan Internet 4G Menggunakan Metode Quality of Service Ibrahim Armadian Pujakesuma; Iwan Iskandar; Novriyanto; Pizaini
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 6 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i6.897

Abstract

This study aims to evaluate the quality of the 4G internet network in Tuah Madani District using the Quality of Service (QoS) method which involves throughput, packet loss, delay, and jitter parameters. Due to the high population level, the Tuah Madani sub-district has a densely populated area with a significant population. This causes the need for high internet services as well. The high population density can affect the quality of the network, where the density of the population and the large number of buildings can cause network performance to be less than optimal. The purpose of this study is to provide comprehensive information about the quality of the 4G internet network in Tuah Madani District and provide recommendations to the public to choose the best provider. Analysis of the quality of the 4G network was carried out in three urban villages with the largest population, namely West Sidomulyo Village, Tuah Karya Village, and Sialang Munggu Village. The results of the study in West Sidomulyo Village showed that Smartfren was the best provider with an index value of 4 and received the "Very Good" category. The results of the study in the Tuah Karya Village showed that Telkomsel was the best provider with an index value of 4 and received the "Very Good" category. The results of the study in the Sialang Munggu Village showed that Smartfren was the best provider in the Sialang Munggu Village with an index value of 4 and received the "Very Good" category
Optimasi Kualitas Jaringan WIFI Fakultas Melalui Redesain Topologi Dengan Menggunakan Network Simulator 2 M. Saski; Iwan Iskandar; Novriyanto; Pizaini
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 2 (2023): Oktober 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i2.1272

Abstract

The utilization of WiFi internet networks on campus as tools to support the learning process at the Faculty of Science and Technology is crucial. Therefore, it essential for the campus to provide internet facilities ensure that all activities, including services and the learning process, are effective. This research analyze the quality of WiFi networks at Faculty of Science and Technology using the Quality of Service (QoS) method with parameters such  throughput, delay, packet loss, and jitter. In this study, testing was conducted on WiFi networks in three buildings within the Faculty of Science and Technology, under different conditions during peak hours and off-peak hours, using several SSIDs such as Pegawai, Pimpinan, Uinsuska, Labor TIF dan Baru Belajar, with bandwidths of up to 100Mbps. Test results indicate that the values obtained for throughput, delay, packet loss, and jitter in the three buildings were categorized as "Excellent" with an index of 4. However, in the Lab building, some parameters were found to be low. Therefore, this research conducted a redesign of the topology in the Lab building using Network Simulator 2 (NS2) to improve the quality of the WiFi network. Four nodes were recommended for each floor of the Lab building in the topology redesign. The results of these tests provided QoS parameter values that were used as information for the tested topology recommendations, showing good parameter quality with a throughput value 4738.7 Kbps, a packet loss value 0%, delay value 3.9639249 ms, and jitter value 0,381779103 ms. The results of this testing can be used as information and analystt for the campus PTIPD to enhance the quality of WiFi networks in the Faculty of Science and Technology
Analisis Manajemen Risiko TI Menggunakan Framework COBIT 5 Domain APO12 dan EDM03 Al Fajri; Novriyanto; Nazruddin Safaat H; Muhammad Affandes
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1396

Abstract

PT Perkebunan Nusantara V Pekanbaru is a state-owned enterprise or BUMN, operating in the field of oil palm and rubber plantations located in the city of Pekanbaru, Riau Province, PTPN V has utilized information technology in running the organization and business processes therein, there are risks- risks that will disrupt the application of information technology and even be detrimental to the company, such as the risk of technical specification errors, the risk of errors in self-estimated price calculations (HPS), the risk of errors in the Work Plan and Requirements (RKS) documents and the risk of server failure or network failure. Risk management is an effort for PTPN V as the basis for infrastructure in good risk management governance, the research was carried out with the aim of being able to carry out information technology risk management analysis and provide recommendations to harmonize risk management in information technology processes, using the COBIT 5 framework and appropriate domains, namely the APO12 and EDM03 domains, The data needed in this research is in the form of secondary data and primary data, in the EDM03 domain it is known that the capability value is 4.56, which means the company has reached capability level 5 (optimizing process), in the APO12 domain it is known that the capability value is 4.43, which means The company has achieved capability level 4 (predictable process). Recommendations are given for processes in the APO12 domain as the domain used for risk management, while for the EDM03 domain process as the domain for risk optimization in the procurement and IT sections of PTPN V Pekanbaru
Analisis Sentimen Masyarakat Terhadap Kenaikan Biaya Haji Tahun 2023 Menggunakan Metode Naïve Bayes Classifier Hertati; Elin Haerani; Novriyanto; Fadhilah Syafria
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1457

Abstract

The Indonesian government through a meeting of the Ministry of Religion and Commission IVIII of the DPR-RI agreed on the cost of organizing the Hajj pilgrimage (BPIH) i1444 iH/2023 iM, an average of IDR 90,050,637.26 per irregular pilgrimage. However, this policy gave rise to various public responses. The public's anger regarding the increase in Hajj fees in 2023 was found on the social media iTwitter. In this study, we conducted a sentiment classification analysis of Tweets to determine public opinion regarding the increase in Hajj costs in 2023 using the naïve Bayes classifier method because this method tends to be simple and easy to use. The data set used was 3000 tweets with a total of 1866 positive data, 415 negative data. This research resulted in an accuracy value of 81.46% in the 70:30 data division, in the 80:20 data division, namely 80.74% and in the data division. 90:10 which is 79.04. In this research, there were more positive responses from the public, this proves that the increase in Hajj costs in 2023 can be accepted by the public. The highest accuracy in this study was 81.46% with a 70:30 data split. It is recommended that further research use other algorithms to see a comparison of the results of different algorithms in classifying public sentiment regarding the increase in the cost of Hajj in 2023.
Co-Authors Aditya Dyan Ramadhan Agesta Putrama Al Fajri Alviandy Syaputra Alvin Alvin Anzaz Islami Alwis Nazir Arvansyah, M Afdhol Darmizal, Teddie Dosmaulina Dosmaulina Elin Haerani Elin Haerani Elin Haerani Elin Haerani (Scopus ID: 57191839502) Elvia Budianita Emir Ramon Fadhilah Syafira Fadhilah Syafria Fadhilah Syafria Fahrul Al Hidayat Fatimah Almira Firdausi Febby Kurniawan Fitra Lestari Fitri Insani Fitri Insani (Scopus ID: 57190404820) Hafsyah hariansyah, Aldi Haris Simare Mare Hartini Hartini Hertati Ibrahim Armadian Pujakesuma Imam Vusuvangat Ismail Marzuki Iwan Iskandar Iwan Iskandar Iwan Iskandar Lesmana, Aria Lola Oktavia Lola Oktavia M. Saski M. Zaid.S Muhammad Affandes Muhammad Affandes, Muhammad Muhammad Aldi Wirayudha Muhammad Irfan Syah Muhammad Irsyad Nazruddin Safaat Nazruddin Safaat H Nazruddin Safaat H Nuraisyah Idham Oktavia, Lola Pizaini Pizaini Pizaini, Pizaini Reski Mai Candra Reski Mai Candra Setio Mulyo Siddik, Muhammad Khoirun Siska Kurnia Gusti Suwanto Sanjaya Suwanto Sanjaya Syofin Wibawa Mukti Teddie Darmizal Teddie Darmizal Vitriani, Yelfi Wenni Syafitri Yusra, Yusra