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All Journal Telematika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Creative Information Technology Journal INTECOMS: Journal of Information Technology and Computer Science METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi JURIKOM (Jurnal Riset Komputer) Journal on Education CCIT (Creative Communication and Innovative Technology) Journal Jurnal Tecnoscienza Infotek : Jurnal Informatika dan Teknologi Infokes : Jurnal Ilmiah Rekam Medis dan Informasi Kesehatan EXPLORE Indonesian Journal Computer Science (ijcs) SmartComp Explore Jurnal Pengabdian Masyarakat (JURDIMAS)
Anggit Dwi Hartanto
Universitas AMIKOM Yogyakarta
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Apriori Algorthm Implementation to Determine Product Sales Priority Anggit Dwi Hartanto; Bobby Candera Lim; Deas Pradana
CCIT Journal Vol 13 No 1 (2020): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (649.11 KB)

Abstract

Nowadays there are so many stores that stand where every day you have a transaction on selling goods. Sometimes transactions per day are not small, these many transactions can lead to a buildup of data. However, it is rare for business owners to realize that the data can be processed into various kinds of information which can later be used for the interests of companies and stores. The solution to this can be to use an algorithm that is often used today, namely the Apriori algorithm. The Priori algorithm is one of the data mining techniques used to find high associative patterns between products. The calculation is done by determining the support and confidence between the itemset set in the transaction or also called the association rule function. The test results with a priori algorithm get results in the form of an association rule, one of which is the connection of Oily Skin Clean Wash items -> Eshter Acne Toner with the highest support value of 1.0000. So it is expected that this can be used as a reference in efficient product sales going forward
Implementation Of The C4.5 Algorithm For Recruitment Of E-Sports Team Members Akhmad Efendi; Anggit Dwi Hartanto
CCIT (Creative Communication and Innovative Technology) Journal Vol 13 No 2 (2020): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (447.885 KB) | DOI: 10.33050/ccit.v13i2.1097

Abstract

Fenomena E-Sport in an increasingly fast-paced world, creating a global culture ranging from international E-Sport tournaments to the birth of management that houses the competing E-Sport teams to be the best. E-Sport teams need players or gamers who have high skills, but it is not only the skill level of a player that determines the success of an E-Sport team in a tournament, there are other factors that determine the success of an E-Sport team, where factors this can be used in determining the decision to recruit players or gamers to become members of the E-Sport team. Decision support systems (DSS) is one of the systems that can be relied upon as a method to assist an organization or E-sports team management in assisting the decision-making process. One method that can be used in DSS is to use the Decision Tree C4.5 Algorithm. The solution technique is to use entropy and information gain for the expansion of decision trees. C4.5 algorithm is a decision tree-based method. In the C4.5 algorithm, the selection of attributes is done using Gain, Ratio, by finding the Entropy value. C4.5 algorithm can provide effective results in supporting a decision
Pemilihan Parameter Terbaik pada Algoritma Winnowing dalam Mendeteksi Tingkat Kesamaan Dokumen Bahasa Indonesia Wahyu Hidayat; Ema Utami; Anggit Dwi Hartanto
Creative Information Technology Journal Vol 7, No 2 (2020): Juli - Desember
Publisher : UNIVERSITAS AMIKOM YOGYAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/citec.2020v7i2.256

Abstract

Pengidentifikasian terkait plagiarisme terhadap dokumen berbahasa Indonesia telah dilakukan di penelitian terkait, untuk pendeteksi tingkat kesamaan dokumen. Dalam penelitian tersebut telah digunakan algoritma pendeteksi kesamaan dokumen dengan metode fingerprint sseperti Algoritma Winnowing. Algoritma Winnowing memiliki perbedaan pada penggunaan parameter seperti ada yang menggunakan k-gram dan n-gram. Dari perbedaan parameter tersebut dilakukan penelitian performa dari perbandingan penggunaan parameter yang berbeda pada pemotongan string pada tahap algoritma Winnowing sehingga dapat diketahui parameter yang mempunyai tingkat performa yang paling baik. Hasil penelitian pada k-gram memiliki tingkat nilai similarity yang tinggi namun ketika nilai jumlah k semakin besar akan mengurangi tingkat nilai similarit dengan rata-rata hasil pada k = 2 sebesar 0.5299, k = 3 sebesar 0.1689, k = 5 sebesar 0.0283 dan k = 7 sebesar 0.0095. Penerapan pemotongan string n-gram pada unigram memiliki rata-rata tingkat similarity sebesar 0.0683, bigram 0.003, pada trigram dan four-gram sebesar 0.000. Pada perbandingan kecepatan pemrosesan waktu k-gram dan n-gram tidak terlihat perbedaan yang signifikan dan keduanya mendominasi selama 6 detik.Kata Kunci—Algoritma Winnowing, Jaccard Similarity, Fingerprint, K-gram, N-gramIdentification related to plagiarism of Indonesian language documents has been carried out in related research, such as for the purpose of detecting the level of similarity documents. In this research, algorithm similarity detection algorithms have been used, especially with the fingerprint method wich Winnowing algorithm. Winnowing algorithm using parameters such as those using k-gram and n-gram. From these different parameters, a study of the performance of the comparison the use of different parameters in the string cutting at the Winnowing algorithm stage can be found out which parameter has the best level of performance. The results of research on k-gram have a high level of similarity value, but when the value of the number of k gets bigger it will reduce the level of similarity values with an average result at k = 2 of 0.5299, k = 3 of 0.1689, k = 5 of 0.0283 and k = 7 in the amount of 0.0095. The application of cutting n-gram strings on unigram has an average similarity level of 0.0683, bigram 0.003, on trigrams and four-grams of 0.000. In the comparison of the processing speed of k-gram and n-gram time, there was no significant difference, and both dominated for 6 seconds. Keywords— Winnowing algorithm, Jaccard Similarity, Fingerprint, K-gram, N-gram
Tinjauan Literatur Sistematik tentang Structural Similarity Index Measure untuk Deteksi Anomali Gambar Halim Bayuaji Sumarna; Ema Utami; Anggit Dwi Hartanto
Creative Information Technology Journal Vol 7, No 2 (2020): Juli - Desember
Publisher : UNIVERSITAS AMIKOM YOGYAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/citec.2020v7i2.248

Abstract

Image enhancement merupakan prosedur yang digunakan untuk memproses gambar sehingga dapat memperbaiki atau meningkatkan kualitas gambar agar selanjutnya dapat dianalis untuk tujuan tertentu. Ada banyak algoritma image enhancement yang dapat diterapkan pada suatu gambar, salah satunya dapat menggunakan algoritma structural similarity index measure (SSIM), algoritma ini berfungsi sebagai alat ukur dalam menilai kualitas gambar, bekerja dengan membandingkan fitur structural dari gambar, dan kualitas gambar dijelaskan oleh kesamaan structural. Selain untuk menilai kualitas suatu gambar, SSIM dapat menjadi metode dalam menganalisis perbedaan gambar, sehingga diketahui anomali dari perbandingan dua gambar berdasarkan data structural dari sebuah gambar. Tinjauan literature sistematis ini digunakan untuk menganalisis dan fokus pada algoritma SSIM dalam mengetahui anomaly 2 gambar yang terlihat mirip secara human visual system. Hasil sistematis review menunjukkan bahwa penggunaan algoritma SSIM dalam menilai kualitas gambar berkorelasi kuat dengan HVS (Human Vision System) dan dalam deteksi anomaly gambar menghasilkan akurasi yang berbeda, karena terpengaruh intensitas cahaya dan posisi kamera dalam mengambil gambar sebagai dataset.Kata Kunci— SSIM, anomaly, gambar, deteksiImage enhancement is a procedure used to process images so that they can correct or improve image quality so that they can then be analyzed for specific purposes. Many image enhancement algorithms can be applied to an image. one of the usable methods is the structural similarity index measure (SSIM) algorithm, this algorithm serves as a measuring tool in assessing image quality. It works by comparing the structural features of images, and the image quality is explained by structural similarity. In addition to assessing the quality of an image, SSIM can be a method of analyzing image differences. So, the anomalies are known from the comparison of two images based on the structural data from an image. This systematic literature review is used to analyze and focus on the SSIM algorithm in knowing anomaly 2 images that look similar to the human visual system. Systematic review results show that the use of the SSIM algorithm in assessing image quality is strongly correlated with HVS (Human Vision System). In anomaly detection of images produces different accuracy because it is affected by light intensity and camera position in taking pictures as a dataset.Keywords— SSIM, anomaly, gambar, deteksi
Survey on Deep Learning Based Intrusion Detection System Omar Muhammad Altoumi Alsyaibani; Ema Utami; Anggit Dwi Hartanto
Telematika Vol 14, No 2: August (2021)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v14i2.1317

Abstract

Development of computer network has changed human lives in many ways. Currently, everyone is connected to each other from everywhere. Information can be accessed easily. This massive development has to be followed by good security system. Intrusion Detection System is important device in network security which capable of monitoring hardware and software in computer network. Many researchers have developed Intrusion Detection System continuously and have faced many challenges, for instance: low detection of accuracy, emergence of new types malicious traffic and error detection rate. Researchers have tried to overcome these problems in many ways, one of them is using Deep Learning which is a branch of Machine Learning for developing Intrusion Detection System and it will be discussed in this paper. Machine Learning itself is a branch of Artificial Intelligence which is growing rapidly in the moment. Several researches have showed that Machine Learning and Deep Learning provide very promising results for developing Intrusion Detection System. This paper will present an overview about Intrusion Detection System in general, Deep Learning model which is often used by researchers, available datasets and challenges which will be faced ahead by researchers
IMPLEMENTASI ALGORITHM CNN DAN SLTM UNTUK TRANSLATOR THINGS Al Abdul Rohim; Taufiq Nashrullah; Anggit Dwi Hartanto; Hartatik
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 6 No. 1 (2020): Maret 2020
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v6i1.249

Abstract

Translator Things is a technology that allows users to find out the names of objects in a variety of foreign languages, by using a cellphone / laptop camera, Translator Things is very useful if we are abroad and we can find out the name of the object that our country is visiting. For the System we use CNN and LSTM as CNN's algorithm to identify objects on the camera, and LSTM to translate text on the identification of these objects The results of this discussion will identify objects that will be identified through text.
Implementasi Algoritma Greedy Pada Game Pacman Pamela Hapsari Putri; Muhammad Ridlo Arifandi; Edy Hardianto Rifeni; Fakhrur Wiradhika; Rumini Rumini; Anggit Dwi Hartanto
JURIKOM (Jurnal Riset Komputer) Vol 5, No 6 (2018): Desember 2018
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (208.124 KB) | DOI: 10.30865/jurikom.v5i6.1001

Abstract

Pacman is an arcade game developed by Namco and released in Japan on May 22, 1980. Pac-Man, which is still popular today, has also been released on other platforms such as Game Boy and SNES. The designer of this game is Toru Iwatani, who is a Namco employee. The concept of the game in the Pacman game is very simple: Players must control the Pacman character to eat all the small dots and other special objects that are in the maze without being caught by 4 ghosts. The greedy algorithm is used to find the current shortest path from the position of the ghost character to the position of the Pacman character.
Literatur Review Bat Algorithm Terhadap Analisis Sentimen Pada Lini Masa Twitter Candra Adipradana; Ema Utami; Anggit Dwi Hartanto
JURNAL TECNOSCIENZA Vol. 5 No. 1 (2020): TECNOSCIENZA
Publisher : JURNAL TECNOSCIENZA

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Abstract

Algoritma metaheuristik seperti particle swarm optimization, firefly algorithm and harmony sekarang menjadi metode yang kuat untuk menyelesaikan banyak masalah optimasi yang sulit. Dalam literature review ini, kami mengusulkan suatu metode metaheuristik baru yaitu Binary Bat Algorithm atau Algoritma Kelelawar dengan Biner, hal ini didasarkan pada perilaku ekolokasi kelelawar. Kami juga berniat untuk menggabungkan keunggulan dari algoritma yang ada ke dalam algoritma kelelawar baru. Setelah perumusan terperinci dan penjelasan implementasinya, kami akan melakukannya perbandingan algoritma yang diusulkan dengan algoritma lain yang ada, termasuk genetic algorithms and particle swarm optimization. Simulasi menunjukkan bahwa algoritma yang diusulkan tampaknya jauh lebih unggul daripada algoritma lainnya, dan kedepannya studi lebih lanjut juga akan dibahas. Kata kunci: Biner, Ekolokasi, Metaheuristik, Algoritma Kelelawar
Klasifikasi Kepribadian Dengan Metode DISC Pada Twitter Menggunakan Algoritma Artificial Neural Network Idris Idris; Ema Utami; Anggit Dwi Hartanto
JURNAL TECNOSCIENZA Vol. 5 No. 1 (2020): TECNOSCIENZA
Publisher : JURNAL TECNOSCIENZA

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Abstract

Maju mundurnya suatu perusahaan biasanya didukung oleh adanya sumber daya yang handal, terutama sumber daya manusia. Perekrutan dan penempatan pegawai pada posisi yang tepat akan membawa dampak yang signifikan bagi suatu perusahaan. Di dunia ini sifat dan karakter manusia sangat beraneka ragam bentuknya. Teori DISC mengklasifikasikan kepribadian menjadi empat tipe yaitu dominance, influence, steadiness dan compliance. Perbedaan karakter setiap tipe tentu saja akan berpengaruh pada gaya perilaku, cara menghadapi tekanan hidup dan juga cara berkomunikasi baik secara langsung maupun dengan media sosial. Melalui sosial media, seseorang dapat meluapkan perasaanya melalui postingan yang diunggahnya. Dari postingan tersebut dapat dilakukan analisis mengenai karakter kepribadian yang ia dimiliki. Penelitian ini bertujuan untuk mengetahui seberapa besar akurasi analisis profiling pada Twitter sehingga bisa menjadi acuan untuk proses perekrutan pegawai. Penelitian ini menggunakan algoritma Artificial Neural Network untuk mengklasifikasikan 275 akun Twitter kedalam teori DISC dan mendapatkan akurasi sebesar 42,91% dari 72 skenario yang dijalankan. Kata kunci: Kepribadian DISC, Media Sosial, Analisis Profiling, Sumber Daya Manusia
PENGENALAN EKSPRESI WAJAH MENGGUNAKAN DEEP CONVOLUTIONAL NEURAL NETWORK Biva Candra Lutfi Adiatma; Ema Utami; Anggit Dwi Hartanto
Jurnal Explore Vol 11, No 2 (2021): JULI
Publisher : Universitas Teknologi Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (800.768 KB) | DOI: 10.35200/explore.v11i2.478

Abstract

Pengenalan ekspresi wajah menjadi salah satu bidang penelitian aktif dalam beberapa tahun terakhir. Pendekatan yang ada saat ini sebagian besar menggunakan metode tradisional seperti SIFT, HOG, LBP, yang diikuti oleh klasifikasi yang dilatih dari data gambar atau video. Sebagian besar mendapatkan hasil yang cukup baik ketika menggunakan data citra yang terkontrol , tetapi tidak bekerja dengan baik pada kumpulan data yang lebih sulit dimana terdapat banyak bagian wajah dengan banyak variasi gambar. Banyak penelitian yang telah mengusulkan kerangka kerja untuk pengenalan ekspresi wajah menggunakan metode deep learning. Meskipun kinerjanya lebih baik, masih banyak ruang untuk perbaikan. Dalam penelitian ini kami mengusulkan pendekatan menggunakan metode deep learning berbasis Deep Convolutional Neural Network (DCNN) dengan variasi parameter yang berbeda. Hasil yang didapatkan setelah 5 kali percobaan training pada dataset FER2013 dengan 4 optimizer berbeda yaitu optimizer Nadam mendapatkan hasil yang sama baiknya dengan Adam dengan akurasi 83%, kemudian diikuti Adamax dengan nilai akurasi 82%, dan optimizer terkahir dengan akurasi 74% adalah SGD. Hasil prediksi terbaik diperoleh ketika menggunakan optimizer Nadam dengan akurasi 83%.
Co-Authors Ahmad Fauzi Akhmad Efendi Al Abdul Rohim Alva Hendi Muhammad Alvina Felicia Watratan Anik Sri Widowati Biva Candra Lutfi Adiatma Bobby Candera Lim Candra Adipradana Deas Pradana Edy Hardianto Rifeni Ema Utami Eny Nurnilawati Fajar Surya Putro Fakhrur Wiradhika Halim Bayuaji Sumarna Hanafi Hanafi Hartatik Idris Idris Ivan Rifky Hendrawan Jangkung Tri Nygroho Kusrini Kusrini Muhammad Ilham Fadila Muhammad Ridlo Arifandi Mulia Sulistiyono Omar Muhammad Altoumi Alsyaibani Pamela Hapsari Putri Rumini Rumini Taufiq Nashrullah Triana Triana Wahyu Hidayat wahyuni, wenti ayu Winarnie