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Inovate : Jurnal Ilmiah Inovasi Teknologi Informasi
Published by Universitas Hasyim Asy'ari
ISSN : 31090117     EISSN : 25487795     DOI : -
Core Subject : Science,
Computer Science & IT
INOVATE: Jurnal Ilmiah Inovasi Teknologi Informasi adalah jurnal ilmiah yang diterbitkan oleh Fakultas Teknologi Informasi, yang bertujuan untuk menampung dan mempublikasikan hasil penelitian yang dilakukan oleh dosen dan mahasiswa dari program studi Teknik Informatika, Sistem Informasi, serta Ilmu Komputer. Jurnal ini berfungsi sebagai wadah untuk pengembangan ilmu pengetahuan di bidang teknologi informasi, dengan penekanan pada inovasi serta penerapan teknologi terbaru dalam menyelesaikan permasalahan yang ada di masyarakat dan industri. Fokus utama jurnal ini meliputi penelitian yang berkaitan dengan bidang teknologi informasi, sistem informasi, rekayasa perangkat lunak, serta ilmu komputer. Setiap artikel yang diterbitkan dalam jurnal ini melalui proses review yang ketat, dengan tujuan memberikan kontribusi yang signifikan terhadap pengembangan teori, konsep, serta aplikasi dalam dunia teknologi informasi. Selain itu, jurnal ini juga bertujuan untuk memfasilitasi kolaborasi antara akademisi dan praktisi guna mendorong implementasi solusi teknologi yang relevan dengan kebutuhan industri serta perkembangan sosial yang dinamis. INOVATE diterbitkan secara berkala dengan frekuensi dua kali setahun dan terbuka untuk publikasi berbagai jenis artikel, termasuk penelitian dasar, pengembangan teknologi, serta studi kasus yang mengangkat inovasi di bidang sistem informasi, kecerdasan buatan, dan teknologi berbasis web maupun mobile.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 8 Documents
 1 
Search results for , issue "Vol 5 No 2 (2021): Maret" : 8 Documents clear
Sistem Pendukung Keputusan Penyaluran Program Bantuan Pangan Non Tunai (BPNT) Metode Simple Additive Weighting (SAW) Di Kelurahan Brudu Kecamatan Sumobito Kabupaten Jombang Rizal, Fatchur; Imam Agung, Achmad; Augusta Jannatul Firdaus, Reza
Inovate Vol 5 No 2 (2021): Maret
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/inovate.v5i2.3092

Abstract

Because poverty is the highlight of one of the economic problems that exist in various countries and even the world and including this country, and the poverty rate is a multidimensional problem. This assistance program is part of the poverty reduction program of the government in the first cluster, namely activities on social protection based on the head of the family in meeting or determining the basic food needs of the less fortunate. This food aid will be distributed to the respective officers who take care of the regency or city in the form of non-cash or what is called in-kind, that is, the assistance will be provided in the form of rice and or eggs that have been provided by agents in each region. This study aims to design or create a decision support system for beneficiaries in Brudu village and to implement a decision support system using the Simple Additive Weighting (SAW) method, which is a method for determining aid distribution. The results of this research are managing data of potential beneficiaries with weighted calculations in order to help Brudu Village, Sumobito District, Jombang Regency, namely by using and implementing a system that is more able to work quickly to get results, is appropriate in assessment, and objectively in a decision making so that the selection produced and issued in these results are valid and truly it can be said to be valid that the community deserves assistance and the level of valid data accuracy in the system for determining non-cash food aid using the Simple Additive Weighting (SAW) method is 83.851% Keywords:Non-cash Food Aid Distribution System, Information System, SAW, Action Research, Website
Klasifikasi Dokumen Skripsi Dengan Menggunakan Text Mining (Studi Kasus: Fakultas Teknologi Informasi) Irfanto, Feri; Dwi Indriyanti, Aries; Bagus Pratama Putra, Dharma
Inovate Vol 5 No 2 (2021): Maret
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/inovate.v5i2.3118

Abstract

Thesis document classification is a data mining method with the aim of categorizing thesis abstracts whose categories are unknown. The purpose of thesis document classification aims to assist students in finding a thesis document that is in accordance with their research by reading the abstract to find out specific category. The research discussed about the application of text mining in the classification of thesis documents with case studies at the Faculty of Information Technology. Text mining is functioned to extract data in the form of text to get information from a collection of documents. In this study using the Naïve Bayes Classifier method, a classification method by calculating probability by adding frequencies with a combination of values in the data set. This method has the aim of classifying the datatesting according to the datatraining attributes. Abstract files processed in this classification are abstract files from IT Faculty students who have graduated. There are 5 categories used, namely SPK, RPL, Data Mining, Image Processing, and System and Network Security. The process of calculating the classification of the thesis document using the Naïve Bayes Classifier method begins with inputting training data, preprocessing, calculating the term frequency (word occurrences), calculating the word probability value from the training data, and the final process is calculating the maximum probability value for each category. The data used in this study were 49 data, 34 of which were used for training data and the remaining 15 were used for testing data. Of the total 15 testing data, 14 data were classified correctly and 1 sample was not classified correctly. The accuracy obtained from the thesis document classification system is 93%. Keywords: Thesis Document Classification, Text Mining, Naïve Bayes Classifier
Rancang Bangun Aplikasi Ealah E- Learning Pengenalan Perograman Dasar Berbasiss Web Dengan Menggunakan Algoritma Edit Distance Pada Koreksi Otomatis Jawaban Essay Firmanda Himawan, Ahmad; Dwi Nuryana, I Kadek; Ali, Mahrus
Inovate Vol 5 No 2 (2021): Maret
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/inovate.v5i2.3119

Abstract

E-learning is an innovative learning medium that must be optimized, so that education delivery will continue to develop. By using E-learning, the problems of space and time that have been an obstacle in learning are no longer a barrier. Apart from that, the existence of e-learning which has the feature of being able to correct essay answers automatically is felt to be able to help teachers to provide grades to students, and students can learn independently and can get more learning material and can communicate with teachers outside the classroom. In this research the USDP (unified software development process) method, Edit Distance algorithm, and Cosine Simmiliarity are used to match the students' answers with the teacher's answer key, the counting process starts from Text prepocessing, Case floading, filtering, and tokenizing, then processing of Edit Distance and Cosine Simmiliarity. The results of this study are in the form of a web-based e-learning application which is expected to help learning, especially for teachers, to make it easy to give grades to students in exams or essay assignments. In the tests conducted by the author with data from 5 essay questions along with the answer keys and student answer data, and the authors conducted 2 application tests using the same data by applying the edit distance algorithm the value = 94.98 while without the edit distance algorithm it got a value of = 78, 04 and the difference in value = 16.94 from the total value = 100. Keywords: E-learning, USDP, unified software development process, Cosine Simmiliarity, Edit Distance, Levenstein Distance, Essay Automatic Correction.
Memprediksi Jumlah Produksi Roti Dengan Menerapkan Metode Monte Carlo Nur Rohmah, Fitri; Arwin Dermawan, Dodik; Andriani, Anita
Inovate Vol 5 No 2 (2021): Maret
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/inovate.v5i2.3120

Abstract

The process of predicting the amount of production is useful for reducing the level of producer losses due to inaccuracy in determining the amount of production, so that the stock of goods which usually experience accumulation or even out of stock is expected no longer. The purpose of this research itself is to design a system using Monte Carlo as a method to predict the amount of bread production. In the Monte Carlo method there is one step in the process using random numbers. The method used to generate random numbers this time is using the Linear Congruent Method (LCM). Hasil method. The results of this research is an application to facilitate the admin of the production sector to predict the amount of bread production for the next day. The prediction of the amount of production for this type of comb bread yields 1461 seeds of bread. The results of testing the accuracy of the Monte Carlo method in predicting the amount of bread production using MAPE, resulting in a fairly small error value of 9.43%. So this research is quite appropriate to be used as a method of predicting the amount of bread production. Keywords: Prediction, Production, Monte Carlo, LCM
Penerapan Algoritma Binary Searching Untuk Pencarian Berkas Pada Sistem Pengarsipan (Study Kasus: Pemerintahan Desa Kedungbetik) Nasruddin, Hammam; Mashuri, Chamdan; Wiratsongko, Radityo
Inovate Vol 5 No 2 (2021): Maret
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/inovate.v5i2.3121

Abstract

Archiving files and searching files that are still using manual or not computerized methods. The purpose of this research is to create a website-based filing system that has a file search feature with a fast and accurate time of the many files that have been archived. To find files quickly and accurately from a lot of data, we need an algorithm, the binary searching algorithm. This algorithm is a search algorithm that can search for files in a fast time and has a lighter amount of computing. From the results of testing the search speed using a binary search algorithm, the data search speed test is 100 document archive data, with the name of the data sought is population data. Searching using an algorithm has a time of 148 / ms while searching without using an algorithm is 799 / ms. From testing the search speed using a binary search algorithm this is 95% Keywords: System, Archiving, Algorithm, Binary Searching, File, Searching
Penerapan Metode Exponential Smoothing Pada Prediksi Dana Donatur Di Lembaga Amil Zakat Ummul Quro Kabupaten Jombang Anggung Mestuti Kaprawiran, Immas; Dwi Nuryana, I Kadek; Augusta Jannatul Firdaus, Reza
Inovate Vol 5 No 2 (2021): Maret
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/inovate.v5i2.3122

Abstract

Donor Fund Prediction is a prediction system that aims to predict donor funds at the Ummul Quro Amil Zakat Institution, Jombang Regency. The Donor Fund Prediction is used to predict the next year based on previous year's data. This study focuses on using exponential smoothing while for the prediction method initially using moving averages. The calculated data is 2014-2018 while 2019 is used for testing prediction errors. In predicting exponential smoothing, alpha constant value which has the smallest error is needed. To get it, several stages are needed, namely, predicting 2014-2019 using a moving average with a constant defined by the user, predicting 2014-2018 with exponential smoothing with an alpha value between 0 to 1, looking for the MAPE (Mean Absolute Percentage Error) value at each value alpha used. After obtaining alpha with the smallest MAPE, the alpha value is used to predict 2019. The test results explain that calculations using a program with the Moving average method and Exponential smoothing successfully predict with an accuracy of 93.32% or only have an error of 6.68% instead of using only the method. The moving average only has an accuracy of 90.25%. Keywords: Prediction, Exponential smoothing, Moving average
Penentuan Sekolah Terdekat Untuk Visitasi Asesor Menggunakan Metode Algoritma K-Means Berbasis Web Zainal Ikhwan, Muhammad; Putra Eka Prismana, IGL; Mashuri, Chamdan
Inovate Vol 5 No 2 (2021): Maret
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/inovate.v5i2.3124

Abstract

The closest school determination to visitation of assessors is a step made to facilitate the assessors to implement school accreditation. School Accreditation is an effort to improve the quality of National Education. The provincial school accreditation body or Madrasah (BAP-S/M) conducts recruitment of assessors and is divided by a number of places or regions to perform visitation. The Research aims to design a nearby school-based website determination System and implement the K-means method for such systems. This method is used to group the data by specifying the number of clusters or previous groups, calculating the centroid Center and grouping the data that has the similarity of variables. This method calculation generates multiple iterations that have cluster values. Of these iterations used the least number of cluster values to determine the group of schools within one province. The result of this research is a nearby school determination information system for the visitation of assessors. The testing of this system was conducted at the school in the province of East Java with the coordinate point as a variable of latitude and longitude coordinates. From the test results with 20 school data data into 3 clusters, obtained the result of cluster 1 with the coordinate center point (-7,213605, 112,769658) amounting to 8 schools, cluster 2 with the coordinate center point (-7,202459, 112,636323) amounting to 7 school and cluster 3 with coordinate center point (-7,249299, 112,636909) amounting to 5 schools. Keywords : K-Means, Clustering, Accreditation, School, Web.
Dashboard Penerimaan Mahasiswa Baru Unhasy Dengan Algoritma Naïve Bayes Classifier Ali, Mahrus
Inovate Vol 5 No 2 (2021): Maret
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/inovate.v5i2.4163

Abstract

Every university certainly expects the number of new student admissions (NSA) to always increase every year to improve the quality of the university. However, does the university carry out an analysis related to the input of new student admissions according to the classification. According to the results of research and research studies conducted by interviewing the new student admissions committee at Hasyim Asy'ari Tebuireng University, Jombang. Whereas every year there has not been a system created for classifying new students based on school origin, student address, school or madrasah level and achievement. Because the classification of this data is used as the main data to analyze the condition of new students who are accepted and used as material for evaluating the new student admissions committee (NSA) and determine strategies for recruiting new students the following year. effective way of clustering technique. The interface is built in the form of a web-based dashboard so that it can be accessed online on the campus intranet and can also be used as the main data for evaluating new student committees at Hasyim Asy'ari Tebuireng University Jombang. Keywords: Algoritma naïve bayes classifier, dashboard.

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