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All Journal Jurnal Ilmiah Kursor Jurnal Edukasi dan Penelitian Informatika (JEPIN) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Sistemasi: Jurnal Sistem Informasi Jurnal Pilar Nusa Mandiri Jurnal Mantik Penusa JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURTEKSI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal Informatika Kaputama (JIK) Jurnal Manajemen Informatika Jayakarta Archive: Jurnal Pengabdian Kepada Masyarakat Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Applied Business and Technology Jurnal Informatika: Jurnal Pengembangan IT The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika
Deny Jollyta
Institut Bisnis Dan Teknologi Pelita Indonesia
Religion Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Public Health Social Sciences

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ANALYSIS OF SLOW MOVING GOODS CLASSIFICATION TECHNIQUE: RANDOM FOREST AND NAïVE BAYES Jollyta, Deny; Gusrianty, Gusrianty; Sukrianto, Darmanta
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.8263

Abstract

Classifications techniques in data mining are useful for grouping data based on the related criteria and history. Categorization of goods into slow moving group or the other is important because it affects the policy of the selling. Various classification algorithms are available to predict labels or class labels of data. Two of them are Random Forest and Naïve Bayes. Both algorithms have the ability to describe predictions in detail through indicators of accuracy, precision, and recall. This study aims to compare the performance of the two algorithms, which uses testing data of snacks with labels for package type, size, flavor and categories. The study attempts to analyze data patterns and decides whether or not the goods fall into the slow moving category. Our research shows that Random Forest algorithm predicts well with accuracy of 87.33%, precision of 85.82% and recall of 100%. The aforementioned algorithm performs better than Naïve Bayes algorithm which attains accuracy of 84.67%, precision of 88.33% and recall of 92.17%. Furthermore, Random Forest algorithm attains AUC value of 0.975 which is slightly higher than that attained by Naïve Bayes at 0.936. Random Forest algorithm is considered better based on the value of the metrics, which is reasonable because the algorithm does not produce bias and is very stable.
Analisis Sistem Jalur Terpendek Menggunakan Algoritma Djikstra dan Evaluasi Usability Marlim, Yulvia Nora; Jollyta, Deny; Saputra, Fandri
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 6, No 1 (2020): Volume 6 No 1
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v6i1.37627

Abstract

Tingginya motivasi masyarakat pengguna kendaraan roda empat pribadi, menyebabkan semakin meningkat pula kebutuhan terhadap bengkel mobil. Usaha bengkel ini tumbuh dan berkembang pada titik-titik strategis hamper di semua kota-kotabesar di Indonesia. seperti Pekanbaru yang memiliki luas ± 632.26 km2 . Akibatnya timbul kesulitan dalam mengetahui letak/ alamat bengkel yang hendak dituju secara cepat dari posisi terdekat. Penelitian ini bertujuan utuk menentukan jalur terpendek menuju lokasi bengkel mobil menggunakan algoritma Djikstra melalui aplikasi berbasis web. Hal ini dapat mempermudah masyarakat ketika mengalami kerusakan mobil dimanapun. Algoritma Djikstra bekerja menggunakan graph dengan prinsip greedy yaitu mencari nilai minimum setiap simpul yang dilalui dengan teknik penelusuran mengunakan Best Fisrt Search (BFS). BFS yaitu dengan menelusuri simpul yang tertinggi (awal) kemudian penelusuran ke simpul dibawahnya. Aplikasi selanjutnya diuji dengan evaluasi usability. 5 komponen usability yang digunakan adalah learnability, effesiency, memorability, error, dan saticfaction yang disusun menggunakan kuisoner. Respondennya terdiri dari masyarakat umum yang dipilih secara acak yang terdiri dari 30 responden. Uji usability menggunakan skala likert yang terdiri dari 14 pertanyaan, dengan teknik perhitunggan menggunakan rata-rata sederhana.  Hasil dari pengujian menunjukan bahwa nilai usability dari aplikasi jalur terpendek adalah 78,5%, hal ini menujukan bahwa aplikasi jalur terpendek bernilai baik. Artinya bahwa responden merasa puas dengan adanya aplikasi ini dan terbantu dalam menentukan bengkel terdekat.
Tinjauan Kasus Model Speech Recognition: Hidden Markov Model Jollyta, Deny; Oktarina, Dwi; Johan, Johan
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 6, No 2 (2020): Volume 6 No 2
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v6i2.39231

Abstract

Teknologi pengenal suara (speech recognition) merupakan teknologi yang berkembang pesat dalam bidang kecerdasan buatan (artificial intelligent). Saat ini, teknologi pengenal suara menjadi hal yang komersil melalui berbagai media teknologi seperti smartphone dan komputer. Salah satu pembentuk struktur pengenal suara agar dapat bekerja pada perangkat tersebut adalah model statistik pengenal suara Hidden Markov Model (HMM). Penerapan HMM pada berbagai kasus menunjukkan bahwa model ini cocok dengan berbagai macam data. Tulisan ini merupakan sebuah tinjauan untuk model HMM yang bertujuan untuk memberikan gambaran dan pemahaman terhadap kinerja HMM melalui rangkuman sejumlah penelitian yang digunakan dalam berbagai data. Penerapan HMM tersebut menunjukkan optimalisasi kinerja HMM dan tinjauan terhadap sejumlah penelitian menunjukkan bahwa tingkat keberhasilan HMM dalam mengenali data mencapai 71.43%.
C5.0 Algorithm Implementation on Web-Based Software and Usability Evaluation Ricky Wijaya; Deny Jollyta
Sistemasi: Jurnal Sistem Informasi Vol 10, No 2 (2021): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (879.67 KB) | DOI: 10.32520/stmsi.v10i2.1260

Abstract

AbstrakPerangkat lunak merupakan alat bantu yang memudahkan pengguna dalam pengolahan data dengan cepat dan tepat. Para pengambil keputusan membutuhkan alternatif perangkat lunak yang dapat digunakan setiap saat dengan teknik klasifikasi data algoritma C5.0 sesuai kriteria yang diinginkan. Namun perangkat lunak yang ada umumnya terdiri dari sejumlah teknik dan belum dapat digunakan secara online. Sebagai salah satu algoritma klasifikasi yang popular dalam ilmu data mining, C5.0 dapat memberikan hasil yang lebih baik. Penelitian bertujuan untuk membangun perangkat lunak yang dapat melakukan klasifikasi data menggunakan algoritma C5.0 berbasis web. Perangkat lunak dapat digunakan oleh siapa saja, terutama para pengambil keputusan. Penelitian ini juga dilengkapi dengan pengujian perangkat lunak usability sebelum digunakan. Hasil pengujian memperlihatkan bahwa perangkat lunak yang dibangun dapat diterima dengan nilai usability 76,892% dan berada pada predikat Baik. Diharapkan melalui penelitian ini, dapat memberikan alternatif perangkat lunak yang mampu menyelesaikan masalah klasifikasi menggunakan algoritma C5.0.Kata kunci: perangkat lunak, klasifikasi, algoritma c5.0, usability AbstractSoftware is a tool that makes it easy for users to process data quickly and precisely. Decision makers need an alternative software that can be used at any time with the C5.0 algorithm data classification technique according to the desired criteria. However, the existing software generally consists of a number of techniques and cannot be used online. As one of the popular classification algorithms in data mining science, C5.0 can provide better results. This study aims to build software that can classify data using the web-based C5.0 algorithm. Software can be used by anyone, especially decision makers. This research is also complemented by testing Usability software before used. The test results showed that the software built can be accepted with a Usability value of 76.892% and is in the Good predicate. It is hoped that through this research, it can provide alternative software that is able to solve classification problems using the C5.0 algorithm.Keywords: software, classification, c5.0 algorithm, usability
ANALISIS PENERAPAN NORMALIZED WEB DISTANCE: TINJAUAN KASUS GOOGLE DAN COMPRESSION DISTANCE DENY JOLLYTA
Jurnal Informatika Kaputama (JIK) Vol 5, No 1 (2021): Volume 5, Nomor 1 Januari 2021
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.1234/jik.v5i1.457

Abstract

Currently, various techniques for measuring the proximity between two objects in the internet network are continuously being developed. The objects in question are in the form of concepts, e-mails, words, and so on. Normalized Web Distance (NWD) has proven to be a simple, yet powerful measure of the semantic linkages between the two concepts. NWD has several approaches according to the object being measured, such as Normalized Google Distance (NGD) and Normalized Compression Distance (NCD). NGD and NCD have a way of determining similarity and calculating the distance to find the similarity of two different measuring objects. This paper shows and provides information on the performance of the two NWD approaches, namely NGD and NCD to facilitate understanding of the use of NGD and NCD on various problems. Correct understanding can put the NGD and the NCD in the right case.
A TOPSIS AND ELECTRE COMPARISON ANALYSIS ON WEB-BASED SOFTWARE Rivensin Rivensin; Deny Jollyta
Jurnal Ilmiah Kursor Vol 11 No 1 (2021)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i1.253

Abstract

Methods in the Decision Support System (DSS) have their own techniques in solving organizational problems. Determining the appropriate DSS method with the problem is a common difficulty experienced by organizations. The performance of a DSS method can be measured in various ways. This research aims to determine the performance of the two DSS methods, specifically Technique for Others Preference by Similarity to Ideal Solution (TOPSIS) and Election at Choix Traduisant La Realite (ELECTRE) which are applied to the best lecturer selection system. The research was carried out on software designed using efficiency as one of the International Organization for Standardization (ISO) 9126. The performance of both methods tested on validity and sensitivity testing. The results showed that the TOPSIS performance was better in terms of efficiency and sensitivity. TOPSIS execution time is 0.0085 seconds faster and has a greater sensitivity value of 2.18% compared to ELECTRE. Validity result gave the best results reaching 100% to ELECTRE. That means, the ELECTRE calculation can be trusted because it has a perfect level of accuracy.
Analysis of Slow Moving Goods Classification Technique: Random Forest and Naïve Bayes Deny Jollyta; Gusrianty Gusrianty; Darmanta Sukrianto
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.8263

Abstract

Classifications techniques in data mining are useful for grouping data based on the related criteria and history. Categorization of goods into slow moving group or the other is important because it affects the policy of the selling. Various classification algorithms are available to predict labels or class labels of data. Two of them are Random Forest and Naïve Bayes. Both algorithms have the ability to describe predictions in detail through indicators of accuracy, precision, and recall. This study aims to compare the performance of the two algorithms, which uses testing data of snacks with labels for package type, size, flavor and categories. The study attempts to analyze data patterns and decides whether or not the goods fall into the slow moving category. Our research shows that Random Forest algorithm predicts well with accuracy of 87.33%, precision of 85.82% and recall of 100%. The aforementioned algorithm performs better than Naïve Bayes algorithm which attains accuracy of 84.67%, precision of 88.33% and recall of 92.17%. Furthermore, Random Forest algorithm attains AUC value of 0.975 which is slightly higher than that attained by Naïve Bayes at 0.936. Random Forest algorithm is considered better based on the value of the metrics, which is reasonable because the algorithm does not produce bias and is very stable.
Penerapan Metode Left Corner Parsing dan Analisis Kontekstual Pada Natural Language Processing Deny Jollyta; Muhammad Zarlis; Gusrianty Gusrianty; Yulvia Nora Marlim
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 7 No 1 (2020): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (68.29 KB) | DOI: 10.35957/jatisi.v7i1.269

Abstract

Natural language translation is very helpful in understanding each word or sentence based on the intended meaning. Variety of languages that are not easy to understand, cause miscommunication. Natural Language Processing (NLP) is the choice to translate human language with computer naturally. As one method of parsing, the left corner parsing describes languages ranging from the largest to the smallest constituent, namely the word. The decomposition is followed by representing the meaning or known as contextual interpretation. This study aims to describe the language using the left corner parsing which is equipped with analysis to determine the purpose of sentence using. The test results showed that the Left Corner Parsing method succeeded in deciphering sentences in accordance with the initial language pattern specified and provided the true contextual analysis and easily understood translation results.
IMPLEMENTATION AND SIGN TEST ANALYSIS FOR MEASURING AR EFFECTIVENESS AS PROMOTION TOOL Deny Jollyta; Muhammad Siddik; Lasman Toni
Jurnal Mantik Penusa Vol. 2 No. 2 (2018): Computer Science
Publisher : Lembaga Penelitian dan Pengabdian (LPPM) STMIK Pelita Nusantara Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (311.248 KB)

Abstract

Many smartphone entrepreneurs compete to promote various brands of smartphones. Conventional promotion systems become a mainstay for almost all smartphone entrepreneurs. As a result, sales turnover tends to decrease because it promotes the same goods in the same way. This research helps improve the promotion system peacefully using technology that can be run through the smartphone itself. Augmented Reality (AR) is technology that is the view of the physical world collaborated between reality and 3D virtual image. AR technology is designed on smartphone models to be an application running on the android platform and built on the Unity 3D program. The AR app will be tested on an android device with 3 different versions using Black Box testing. In addition, AR impact analysis is done on smartphone sales to see the response of potential customers to promotion system. The analysis is done by Sign Test method. Over all, the results of this study indicate that the application of AR is successfully tested on all versions of android and the analysis of this impact by Sign Test is good enough to smartphone entrepreneurs promotion system and get a pretty good response from prospective customers.
IMPLEMENTASI ALGORITMA APRIORI DAN FORECASTING PADA TRANSAKSI PENJUALAN Irvan Firnando; Dixsen Dixsen; Tony Tony; Vincent Wijaya; Surianto Surianto; Eri Yanto; Deny Jollyta
Jurnal Mantik Penusa Vol. 3 No. 3 (19): COmputer Science
Publisher : Lembaga Penelitian dan Pengabdian (LPPM) STMIK Pelita Nusantara Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (176.206 KB)

Abstract

One of the most important parts of a retail business or product distribution company is inventory management. Transactions with very large amounts in a certain period make the transaction data on sales, prices, and availability of goods must be managed properly. This study was delivered to facilitate the company in determining policies related to sales and availability of goods through the purchase pattern of association rules and sales predictions using the Moving Average method. Association rule is data mining techniques contained in the Apriori algorithm. This algorithm is able to shows random relationships in a number of transactions. The test resulted in three patterns of purchasing goods with the highest frequency namely Milo Activ-go UHT Cmbk 36x115ml, Bear Brand RTD Milk 30x189ml and Milo Activ-Go UHT Cmbk 36x190ml with values of 46.17%, 41.97% and 15.39%. The Moving Average result, sales predictions produce a total of 3669, 3280, and 2619 for each item that can be prepared in the next period. This can be a company's reference in predicting goods that are in demand or not, determine the number of sales and prioritize the procurement of goods based on the rules of the association produced.
Co-Authors Adiya, M. Hasmil ahmad kamal, ahmad Ahmad Robbiul Iman Akwilla, Hiskia Alyauma Hajjah Andreas Halim Tandiano Astri , Renita Ayu Dasriani, Ni Gusti Buaton, Relita Dadang Priyanto Dadang Priyanto Dafwen Toresa Darmanta Sukrianto Darmanta Sukrianto Des Suryani Des Suryani Dewi Nasien Dixsen Dixsen Dwi Oktarina Dwi Oktarina Dwi Oktarina Eri Yanto Erick Santoso Erlin Erlin Fauzi, Achmad Fenly, Fenly Gusrianty Gusrianty Gusrianty Gusrianty Gusrianty Gusrianty, Gusrianty Gustientiedina Hadi Dwi Putra Hairani Hairani Herman Mawengkang Hutri Rizkiyah Alda Irvan Firnando Jeffry Kody Johan Johan Johan Johan Kadim, Lina Arliana Nur Kody, Jeffry Lasman Toni Loneli Costaner Marlim, Yulvia Nora Muhammad Siddik Muhammad Siddik Muhammad Zarlis Muhammad Zarlis, Muhammad Mukhsin Mukhsin N Novriyenni Oktariana, Dwi Pratama, Teddy Prihandoko . Prihandoko Prihandoko Rahmadian Yuliendi, Rangga Ramalia Noratama Putri Ramdhan, William Ricky Wijaya Rivensin Rivensin Ryan Christian Sabna, Eka Saputra, Fandri Siddik, Muhammad Sukrianto, Darmanta Surianto Surianto Syahril Effendi Tandiano, Andreas Halim Taslim Taslim Teddy Pratama Tony Tony Toresa, Dafwen Triwijoyo, Bambang Krismono Vincent Wijaya Wahyu Joni Kurniawan Wilda Susanti Willy Fernando