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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) IPTEK The Journal of Engineering IPTEK Journal of Science TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi Seminar Nasional Informatika (SEMNASIF) Jurnal Sisfokom (Sistem Informasi dan Komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Infotekmesin Halal Research Journal Jurnal Locus Penelitian dan Pengabdian Sewagati
Aris Tjahyanto
Institut Teknologi Sepuluh Nopember
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Other

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PERANCANGAN MODEL TATA KELOLA KETERSEDIAAN LAYANAN TI MENGGUNAKAN FRAMEWORK COBIT PADA BPK-RI Lukman Hadi Dwi Purnomo; Aris Tjahyanto
Seminar Nasional Informatika (SEMNASIF) Vol 1, No 5 (2010): Information System And Application
Publisher : Jurusan Teknik Informatika

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

Abstract

Penerapan Teknologi Informasi pada sebuah organisasi memerlukan sumber daya yang besar tidak hanya finansial, juga waktu dan energi. Resiko terjadinya kegagalan juga tidak bisa dikatakan kecil. Namun di samping itu, penerapan Teknologi Informasi juga memberikan peluang-peluang untuk peningkatkan produktifitas organisasi yang sudah berjalan. Badan Pemeriksa Keuangan – Republik Indonesia (BPK-RI) adalah lembaga negara yang bertugas memeriksa pengelolaan dan tanggung jawab keuangan negara. Untuk dapat mewujudkan visi dan misinya, TI memberikan kontribusinya dengan menjalankan peran strategis yang dirumuskan dalam Rencana Strategis TI BPK-RI. Salah satu kebutuhan bisnis yang penting adalah mengelola TI sehingga dapat memiliki kapabilitas dan ketersediaan yang mencukupi, sehingga dapat menjadi medium komunikasi bagi para stakeholder-nya. Untuk itu diperlukan panduan yang dapat menjadi acuan dalam mengelola ketersediaan layanan TI. Dari hasil penelitian, diketahui bahwa proses-proses TI yang terkait dengan ketersediaan layanan TI yaitu DS3 (Manage Performance and Capacity) dan DS4 (Ensure Continuous Service) sebagian besar berada pada tingkat kedewasaan 2 (Repeatable but Intuitive). Sedangkan manajemen mengharapkan bahwa sebagian besar atribut pada proses-proses tersebut minimal berada pada tingkat kedewasaan 4 (Managed and Measurable). Untuk mengatasi kesenjangan tersebut, pada penelitian ini disusun rekomendasi-rekomendasi yang bertujuan untuk meningkatkan kematangan sesuai yang diharapkan. Rekomendasi juga dilengkapi dengan outcome measure dan performance indicator serta draft kebijakan yang dapat menjadi panduan dalam mengelola ketersediaan layanan TI berdasarkan kerangka kerja COBIT.
Comparative Analysis of Feature Selection Method to Predict Customer Loyalty Heni Sulistiani; Aris Tjahyanto
IPTEK The Journal of Engineering Vol 3, No 1 (2017)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23378557.v3i1.a2257

Abstract

The growth of Fast Moving Consumer Goods (FMCG) industry is still showing double-digit and Indonesia becomes a potential market for the products FMCG, so that the competition between companies will be intense. The company have to attempted to survive, one of the way is to maintain customer loyalty. Data mining techniques can be used to predict customer loyalty. In data mining pra-processing, feature selection is one of the important thing to reduces the number of features, removes irrelevant, redundant, or data noise, and brings the immediate effects for applications: speeding up a data mining algorithm, improving mining performance such as the accuracy of the prediction and the comprehensive result. This paper aims to identify the relevant factors that affect the performance of the classification of customer loyalty with several feature selection method and to compare the classification performance in customers loyalty prediction of FMCG products. Data was obtained from the results of fast moving consumer goods customers questionnaires towards several brands of instant noodles in Lampung that was ranked TOP Brand Award Phase 1 2016, using nonprobability sampling method and convenience sampling technique. The result in this paper, chi square feature selection methods with threshold > 0.01 showed the best results, it is indicated by the highest accuracy of  random forest classification algorithm, that is 83.2% for thirteenth features
Tax Complaints Classification on Twitter Using Text Mining Prita Dellia; Aris Tjahyanto
IPTEK Journal of Science Vol 2, No 1 (2017)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (588.215 KB) | DOI: 10.12962/j23378530.v2i1.a2254

Abstract

Twitter growth and utilization encourage the emergence of limitless textual information so that people can express their complaints easily This leads the Directorate General of Taxation uses twitter to deal with tax complaints faced by the community. However, the messages on twitter can contain any information, either the tax complaint or not. This will cause difficulties in handling complaints process. It is important to automatically identify so tax complaint handling can be done effectively and efficiently. Given these problems, it is necessary to do the twitter tax complaint classification with the support of text mining. There are several methods of classification such as Naïve Bayes classifiers, Support Vector Machine (SVM) and Decision Tree. This research aims to classify the tax complaint on twitter automatically by using text mining. The experimental results show the value of f-measure of SVM, Naïve Bayes and Decision Tree, respectively, are 89.3%, 85.6% and 76.9%
An Enhanced Dynamic Signature Verification using the X and Y Histogram Features Aris Tjahyanto; Ano Rangga Rahardika; Ary Mazharuddin Shiddiqi
Infotekmesin Vol 12 No 2 (2021): Infotekmesin: Juli 2021
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v12i2.668

Abstract

Dynamic signature verification by using histogram features is a well-known signature forgery detection technique due to its high performance. However, this technique is often limited to angular histograms derived from vectors containing two adjacent points. We propose additional new features from the X and Y histograms to overcome the limitation.  Our experiments indicate that our technique produced Under Curve Area AUC values 0.80 to detect skilled forgery and 0.91 for random forgery. Our method performed best when the verification system uses 12 of the most dominant features.  This setup produced AUC values of 0.80 to detect skilled forgery and 0.93 for random forgery. These results outperformed the original technique when the X and Y histogram features are not used that produced AUC values of 0.78 to detect skilled forgery and 0.90 for random forgery.
Peningkatan Performa Klasifikasi Machine Learning Melalui Perbandingan Metode Machine Learning dan Peningkatan Dataset Fikri Baharuddin; Aris Tjahyanto
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 11, No 1 (2022): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v11i1.1337

Abstract

Classification using machine learning is an alternative that is widely used to classify data. There are various classification methods or also known as machine learning classification algorithms that can be used. However, to get the best classification results, we need a classifier that fits the dataset type to provide the best classification performance. In addition, the quality and quantity of data contained in a dataset also has an influence on the classification performance. In this study, several attempts were made to improve the classification performance of the dataset of Indonesian language exam questions at the elementary school level based on the category of difficulty level. The efforts made consist of improving the quality of the dataset and using the StringToWordVector filter algorithm to manage textual data, as well as the use of several classification algorithms such as the nave Bayes algorithm, Random Forest, and REPTree. Classification is done by using WEKA Tools. The results of the experiments carried out showed the highest performance increase of 15% after improving the quality of the dataset and using the right classification method.
Peningkatan Kinerja Pengklasifikasi Objek Bawah Laut dengan Deep Learning Aris Tjahyanto; Faisal Johan Atletiko
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 21 No 3 (2022)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (258.838 KB) | DOI: 10.30812/matrik.v21i3.1466

Abstract

Pengenalan objek bawah laut dapat dilakukan berdasarkan pola hamburan SONAR, seperti untuk deteksi ranjau dan deteksi batu yang terletak di dasar laut. Kesulitan yang dihadapi pada pengenalan objek bawah laut antara lain adalah pemilihan metode ekstraksi fitur, adanya rotasi objek yang menghasilkan pola hamburan yang berbeda, lingkungan atau latar belakang bervariasi, dan kemampuan pengklasifikasi yang berbeda untuk lingkungan yang lebih kompleks. Pada penelitian ini, kami menggunakan deep learning neural network untuk meningkatkan kinerja klasifikasi dua buah objek bawah laut. Secara khusus, dibandingkan jumlah neuron pada lapisan tersembunyi dan fungsi aktivasi yang dapat menghasilkan kinerja yang lebih tinggi dari penelitian sebelumnya. Pada penelitian sebelumnya, proses klasifikasi dilakukan dengan menggunakan neural network dengan 12 buah lapisan tersembunyi, dan menghasilkan akurasi maksimal sebesar 90.4%. Dilakukan percobaan pada struktur jaringan syaraf tiruan berupa multilayer perceptron dengan 2 buah lapisan tersembunyi dan 7 macam fungsi aktivasi. Dari percobaan yang dilakukan diperolehbahwa deep learning neural network memberikan rata-rata akurasi terbaik sebesar 85,9% dengan akurasi maksimal sebesar 96,15% lebih baik dibandingkan hasil penelitian sebelumnya. Akurasi terbaik tersebut diperoleh dengan memanfaatkan jumlah neuron pada lapisan tersembunyi sebanyak 140 buah, dan fungsi aktivasi reLU untuk lapisan tersembunyi fungsi aktivasi Linear untuk lapisan output.
Pendampingan Sertifikasi Halal Program Self Declare pada UMKM Binaan Koperasi Wanita Setia Bhakti Wanita Surabaya Daril Ridho Zuchrillah; Lily Pudjiastuti; Achmad Dwitama Karisma; Atria Pradityana; Aris Tjahyanto
Sewagati Vol 7 No 4 (2023)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v7i4.540

Abstract

Koperasi merupakan salah satu pilar dari perekonomi Indonesia dimana tujuannya adalah mensejahterakan anggota-anggotanya. Fungsi koperasi di Indonesia beragam mulai dari sebagai tempat simpan pinjam hingga wadah pembinaan usaha mikro kecil dan menengah. Koperasi Wanita Setia Bhakti Wanita Surabaya berdiri sejak 1975 merupakan koperasi Wanita yang memiliki binaan UMKM yang cukup banyak. Namun kesadaran yang dimiliki oleh UMKM binaan Kopwan SBW terkait sertifikasi halal cukup rendah. Hal tersebut terbukti di tenan pujasera di lingkungan kantor Kopwan SBW yang masih belum memiliki NIB hingga sertifikat halal. Sehingga tim abdimas dari ITS mendampingi UMKM binaan Kopwan SBW dengan 4 strategi yaitu menjadikan mahasiswa KKN untuk pendamping halal, melatih para UMKM dengan mengikuti pelatihan Kader Penggerak Halal untuk menjadi penyelia halal, pendampingan pengisian SJPH melalui program Self Declare yang merupakan program percepatan dan gratis oleh pemerintah, serta diakhiri dengan pemberian plakat binaan dari Pusat Kajian Halal ITS Surabaya. Hasil diskusi dengan pengurus Kopwan SBW telah diberikan 10 UMKM yang siap produknya bisa didampingi untuk disertifikasi halal. Kegiatan ini memenuhi dalam tujuan pengembangan keberlanjutan sesuai nomor 2 dan 3 yaitu mengakhiri kelaparan, mencapai ketahanan pangan dan nutrisi yang lebih baik dan mendukung pertanian bekerlanjutan dan memastikan kehidupan yang sehat dan mendukung kesejahteraan bagi semua untuk semua usia.
Pendampingan Kader UMKM Dukuh Menanggal Surabaya Raih Sertifikasi Halal Zuchrillah, Daril Ridho; Altway, Saidah; Hamzah, Afan; Karisma, Achmad Dwitama; Atletiko, Faizal Johan; Trisanti, Prida Novarita; Tjahyanto, Aris; Tetrisyanda, Rizky; Nugroho, Muhammad Aldi; Triastuti, Warlinda Eka
Sewagati Vol 8 No 2 (2024)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v8i2.993

Abstract

Masalah belum dimilikinya sertifikat halal dari produk yang dijual oleh UMKM saat ini menjadi hambatan untuk proses pemasaran produk yang lebih luas. Salah satunya UMKM kader PKK kelurahan Dukuh Menanggal yang berada di Kecamatan Gayungan, Kota Surabaya. Kurangnya informasi akan proses sertifikasi halal sangatlah berdampak pada proses percepatan sertifikasi halal. Oleh sebab itu, guna mempercepat sertifikasi halal di UMKM kader PKK Kelurahan Dukuh Menanggal, tim pengabdian masyarakat beserta mahasiswa KKN dari Institut Teknologi Sepuluh Nopember melakukan pendampingan mitra UMKM untuk mendapatkan sertifikasi halal atas produk yang mereka jual. Pada pengabdian ini beberapa kegiatan dilakukan, seperti Forum Group Discussion, sosialisasi, pelatihan kader penggerak halal, pendampingan UMKM untuk sertifikasi halal, dan pemetaan produk UMKM yang telah bersertifikat halal. Hasil dari pengabdian masyarakat ini yaitu tim abdimas beserta mahasiswa KKN telah mendampingi sebanyak 30 UMKM yang mengajukan sertifikasi halal dengan skema self declare. Kegiatan pendampingan ini diharapkan untuk dilanjutkan kepada para penjual makanan dan minuman di kawasan Sentra Wisata Kuliner (SWK) Surabaya.
Automatic Categorization of Multi Marketplace FMCGs Products using TF-IDF and PCA Features Indasari, Sri Suci; Tjahyanto, Aris
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 2 (2023): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i2.1621

Abstract

The use of technology in line with the increasing number of internet users has caused a shift in the product sales ecosystem to the realm of electronic commerce (electronic commerce). A total of 73.23 customers made purchase transactions using e-commerce and the most purchased products were products classified as Fast Moving Consumer Goods (FMCGs). The increasingly varied FMCGs data coupled with the increasing number of marketplaces is felt to need to be broken down into specific groups. The process is carried out by analyzing e-commerce product information, especially product names, and descriptions. In this study, we propose an automatic categorization of multiple marketplaces using data from multiple marketplaces. Data text is converted into structured data with a series of preprocessing, and comprehensive experiments are carried out to see the extraction performance of variables including TF-IDF, BOW, and N-Gram.  All three methods are used to validate text data sets with K-Means grouping results used with the help of PCA to reduce data dimensions.  The results show that the performance of the TF-IDF algorithm with a dimension reduction value of 70 and the use of Python can provide optimal results for the percentage of grouping data.
Comparative Analysis: Machine Learning Algorithms for TOC Prediction in Pharmaceutical Water Treatment Systems Mustapa, Dieki Rian; Tjahyanto, Aris
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2148

Abstract

Water quality is crucial in pharmaceutical production, where it serves as a solvent and raw material. Contamination with organic compounds poses a risk to product integrity and safety. TOC serves as a key indicator for assessing organic pollution levels in water. An increase in TOC signals potential issues with water treatment systems. Machine learning prediction of TOC values is essential for preemptive monitoring and maintenance. This study aimed to compare three different machine learning algorithms - Linear Regression (RL), Random Forest (RF), and multilayer perceptron (MLP) - for predicting Total Organic Carbon (TOC) in pharmaceutical water treatment systems. By utilizing a dataset covering various operational conditions of pharmaceutical water treatment systems, the research conducted a comprehensive analysis. Each algorithm underwent evaluation using performance metrics like coefficient of determination (R-squared), and prediction accuracy to assess their effectiveness in predicting TOC concentrations. A correlation coefficient approaching 1 (100%) signifies a strong relationship between model predictions and actual target values (accuracy prediction), while a smaller Mean Absolute Error (MAE) indicates higher accuracy in predicting target values. The study found that the results of the correlation coefficient in order from highest to lowest are the RF, MLP, and RL models with values of 95.04%, 93.11%, and 80.27%, respectively. Likewise, additional metrics for evaluation, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Relative Absolute Error (RAE) and Root Relative Squared Error (RRSE), exhibit a ranking from lowest to highest values across RF, MLP, and RL models. RF has a higher prediction accuracy of the TOC than other models (95%) and lowest MAE (3.9). This research offers valuable insights into utilizing machine learning algorithms for TOC prediction within pharmaceutical water treatment to make informed decisions, improving water treatment systems and overall product quality.
Co-Authors Achmad Dwitama Karisma Afan Hamzah, Afan Afian Syafaadi Rizki Ali Altway Anny Yuniarti Ano Rangga Rahardika Ary Mazharuddin Shiddiqi Atletiko, Faizal Johan Atria Pradityana Cahyono Cahyono Diah P Wulandari Diah P. Wulandari Faisal Johan Atletiko Fikri Baharuddin Hanim Maria Astuti Heni Sulistiani Imam Kuswardayan Indasari, Sri Suci Irmasari Hafidz Krisma Marinda Sormin Siregar Lily Pudjiastuti, Lily Lukman Hadi Dwi Purnomo Mazharuddin Shiddiqi, Ary Mukti, Bayu Siddhi Mustapa, Dieki Rian Naseela, Qudsiyah Zahra Ilham Ni Putu Rina Y. Nugroho, Muhammad Aldi Nurul Fajrin Ariyani Nurul Hidayat Pratama, Rivanda Putra Prida Novarita Trisanti, Prida Novarita Prita Dellia Purnama, I Putu Adhitya Pratama Mangku Rahmat Trialih Rangga Rahardika, Ano Riskandy, Yudi Hendra Sujianik Sujianik Tetrisyanda, Rizky Warlinda Eka Triastuti Yoyon K Suprapto Yoyon K. Suprapto Yudistira, Ahmadhian Daffa Zuchrillah, Daril Ridho Zuchrillah, Daril Ridho