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All Journal Jurnal Teknik ITS IPTEK Journal of Proceedings Series Jurnal Buana Informatika PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic JUTI: Jurnal Ilmiah Teknologi Informasi Scan : Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi International Journal of Artificial Intelligence Research JURNAL ILMIAH GEOMATIKA Journal of Sustainable Technology and Applied Science Jurnal Indonesia Sosial Teknologi International Journal of Organizational Behavior and Policy Sewagati Journal of Informatics Development
Ginardi, R.V. Hari
Institut Teknologi Sepuluh Nopember
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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Enhancing Electricity Consumption Prediction with Deep Learning through Advanced Data Splitting Techniques Pratiwi, Adinda Putri; Ginardi, Raden Venantius Hari; Saikhu, Ahmad
International Journal of Artificial Intelligence Research Vol 8, No 2 (2024): December 2024
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i2.1204

Abstract

Energy consumption is increasing due to population growth and industrial activity, making electricity essential in human life. With limited natural resources, effective management of electrical resources is crucial to reduce energy usage amidst rising demand. The current trends on using deep learning as prediction can enhance the performances. To have good performance it needs correct preprocessing data, so it will produce a model with less overfitting. This research proposes a model using time-series cross-validation as the splitting data and correlation to choose the best features set for the prediction of electricity consumption. Experiments will compare time-series cross-validation and holdout methods to see the performances of splitting data and enhancing the multi-horizon data.  The experiment used 8 sets of feature lists, which are paired in combination based on correlation to ensure the best features that are related. The result is splitting data using time-series cross-validation can maintain good perfomances on mode and holdout can maintain a good evaluation performance across the horizon. Feature sets that include temporal features have excellent results, especially when combined with features that have the strongest correlation relationship with electricity consumption, leading to an enhanced R2. Among all the models tested, CNN-GRU had the best model for multistep prediction across various every horizons and feature sets.
Smart City Maturity Analysis Based on COBIT 2019 and SNI ISO 37122:2019 Ahkam, Syuaib; Ginardi, R. V. Hari
International Journal of Organizational Behavior and Policy Vol 4 No 2 (2025): JULY 2025
Publisher : Accounting Department, School of Business and Management - Universitas Kristen Petra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/ijobp.4.2.53-64

Abstract

In the current era of digital transformation, the development of Smart City is crucial for regions that want to improve public services, stimulate economic growth, and improve the quality of life of their citizens. West Sumbawa Regency, with its tourism and creative economy potential, has adopted the Smart City initiative. However, its effectiveness is hampered by suboptimal IT governance, limited digital infrastructure, and a lack of standardized integrated evaluation models. This study aims to analyze and assess the maturity of Smart City in West Sumbawa Regency by combining the COBIT 2019 framework for IT governance and SNI ISO 37122:2019 for smart city performance indicators. Using a mixed-methods approach—including a survey of 150 stakeholders for quantitative analysis and in-depth interviews with 50 key informants for qualitative analysis—as well as PLS-SEM analysis, capability maturity assessment, and GAP analysis, the results show that most IT governance processes are at maturity levels 2–3. This indicates a significant gap between existing IT governance practices and the achievement of Smart City indicators, particularly in aligning corporate objectives and risk management. The main contribution of this research is the development of an integrated evaluation model that provides a holistic evidence-based roadmap for local governments to formulate more effective Smart City policies to achieve sustainable smart city transformation.
Identifikasi Penyakit pada Daun Tebu dengan Gray Level Co-Occurrence Matrix dan Color Moments Dewi, Ratih Kartika; Ginardi, R.V. Hari
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 1 No 2: Oktober 2014
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (819.699 KB) | DOI: 10.25126/jtiik.201412114

Abstract

Abstrak Karat dan mosaik adalah penyakit pada tebu yang menyerang tebu di Indonesia dan menimbulkan kerugian. Teknologi informasi untuk deteksi penyakit tebu diperlukan dalam menunjang peningkatan produksi tebu yang dapat menghasilkan panen optimal. Penelitian yang berkembang dalam identifikasi penyakit tanaman melalui identifikasi citra digital daun belum ada yang khusus membahas tebu, tetapi mengenai penyakit tanaman secara umum. Penelitian ini membangun sistem identifikasi penyakit pada daun tebu melalui identifikasi citra digital daun dengan pemilihan fitur tekstur dan warna melalui gray level co-occurrence matrix (GLCM) dan color moments. Tahap awal penelitian adalah pengumpulan data citra daun tebu berpenyakit dari survei lapangan. Tahap selanjutnya adalah pre-processing citra untuk dapat diolah ke tahap selanjutnya yaitu ekstraksi fitur. Ekstraksi fitur tekstur dilakukan dengan gray level co-occurrence matrix (GLCM) dan ekstraksi fitur warna dengan color moments. Klasifikasi dilakukan berdasarkan fitur yang telah diekstraksi sebelumnya. Penelitian ini menggunakan metode klasifikasi support vector machine (SVM). Pengujian dilakukan untuk mengetahui fitur yang kemunculannya menyebabkan perubahan dalam hasil klasifikasi dengan 4 skenario meliputi penghapusan fitur bentuk, pemilihan fitur tekstur, pemilihan fitur warna, dan kombinasi fitur tekstur dan warna. Kombinasi fitur tekstur dengan GLCM correlation, energy,  homogeneity dan variance bersama fitur warna dengan color moments 1,2 dan 3 yang diuji pada skenario 4 merupakan kombinasi fitur yang direkomendasikan untuk identifikasi penyakit pada daun tebu dengan akurasi 97%. Kata kunci: ekstraksi fitur, penyakit tebu, citra daun, GLCM, dan color moments. Abstract Mosaic and rust are sugarcane diseases that happen in Indonesia and has considerable economic impact. Information technology for sugarcane disease detection is useful in supporting optimal sugarcane production. Most of current researches are about plant disease identification in general. There is no specific research about identification of sugarcane disease. This research proposes a sugarcane disease identification from sugarcane leaf image with gray level co-occurrence matrix (GLCM) and color moments. This research begins with collecting data from field survey. After sugarcane leaf images are captured through a field survey, they are pre-processed in order to be used in the features extraction step. Extracted features from these images are texture and color. Texture feature extraction is conducted by GLCM while color feature extraction is conducted by color moments. Classification method which is used in this research is support vector machine (SVM). Test conducted to find distinctive feature that has a significant impact in classification, there are 4 scenario to test the effects in deletion of shape feature, selection of texture and color feature, and also combination of texture and color feature. Texture feature with GLCM correlation, energy,  homogeneity and variance combined with color moments 1, 2 and 3 for color feature extraction in 4th scenario is an appropriate feature for identification of sugarcane leaf disease with 97% classification accuracy. Keywords: feature extraction, sugarcane disease, leaf image, GLCM and color moments.
Komparasi Kinerja Algoritma C4.5, Gradient Boosting Trees, Random Forests, dan Deep Learning pada Kasus Educational Data Mining Mutrofin, Siti; Machfud, M. Mughniy; Satyareni, Diema Hernyka; Ginardi, Raden Venantius Hari; Fatichah, Chastine
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 4: Agustus 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020742665

Abstract

Penentuan jurusan di SMA Negeri 1 Jogoroto, Jombang, Jawa Timur menggunakan kurikulum 2013, di mana penentuan jurusan siswa tidak hanya melibatkan keinginan siswa, tes peminatan yang dilakukan siswa di SMA pada minggu pertama, tetapi juga dilengkapi dengan nilai siswa semasa di SMP (nilai rapor siswa, nilai Ujian Nasional, serta rekomendasi guru Bimbingan Konseling), rekomendasi orang tua siswa. Selama ini, sekolah menggunakan proses konvensional dalam menentukan jurusan, yaitu menggunakan Microsoft Excel, yang cenderung lama serta rawan akan kekeliruan dalam melakukan penghitungan. Penentuan jurusan ini dilakukan setiap awal ajaran baru pada siswa baru kelas X. Rata-rata setiap tahun, sekolah mengelola siswa sejumlah 290 dengan waktu dan sumber daya manusia yang terbatas. Pada penelitian ini, penggunaan algoritma ID3 tidak cocok karena data bertipe numerik, sedangkan ID3 hanya mampu menggunakan data bertipe nomial maupun polinomial, sehingga diganti algoritma C4.5. Namun, beberapa penelitian mengatakan algoritma C4.5 memiliki kinerja kurang bagus dibandingkan algoritma Gradient Boosting Trees, Random Forests, dan Deep Learning. Untuk itu, dilakukan perbandingan antara keempat metode tersebut untuk melihat keefektifannya dalam menentukan jurusan di SMA. Data yang digunakan pada penelitian ini adalah data penerimaan siswa baru tahun ajaran 2018/2019. Hasil dari penelitian ini menunjukkan jika atribut yang digunakan bertipe polinomial dengan Deep Learning memiliki kinerja paling unggul untuk semua algoritma jika menggunakan fungsi activation ExpRectifier. Sedangkan jika atributnya bertipe numerik, Deep Learning memiliki kinerja paling unggul untuk semua algoritma jika menggunakan fungsi Tanh untuk semua random sampling. Namun, Deep Learning memiliki kinerja paling buruk untuk semua algoritma jika menggunakan loss Function berupa absolut.  Abstract In SMAN 1 Jombang, East Java, the process of determining the students’ majors referred to the 2013 curriculum in which not only the students’ own choices and specialization tests conducted in their first week of SMA were considered but also the student’s SMP grades (a report card, UN scores, and counseling teacher’s recommendation) and parents' recommendation. So far, the school had used Microsoft Excel which required a long time to do and was prone to calculation errors in the process of determination. The process was carried out, with limited time and human resources, at the beginning of a new academic year for grade X students, consisting of 290 students on average. In this present research, the use of ID3 algorithm was not suitable because of its numeric data type instead of nominal or polynomial data. Thus, the C4.5 algorithm was applied, instead. However, the performance of C4.5 algorithm was proved lower than the algorithms of Gradient Boosting Trees, Random Forests, and Deep Learning. Hence, a comparison of performance between them was done to see their effectiveness in the process. The data was the list of new students of the academic year 2018/2019. The results showed that if the attributes are polynomial, the Deep Learning algorithm had the best performance when using the ExpRectifier activation function. When they were numeric, Deep Learning has the most superior performance when using the Tanh function. However, Deep Learning has the worst performance when using the loss function in the form of absolute.
Pengembangan Metode Information Retrieval dan Haversine Formula untuk Rekomendasi Penentuan Klinik di Kabupaten Jember Hizham, Fadhel Akhmad; Ginardi, Raden Venantius Hari
Journal of Informatics Development Vol. 1 No. 1 (2022): Oktober 2022
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v1i1.896

Abstract

Klinik merupakan fasilitas tempat orang berobat dan memperoleh advis medis serta tempat mahasiswa kedokteran melakukan pengamatan terhadap kasus penyakit yang diderita para pasien. Saat ini, hadirnya virus Corona (COVID-19) membuat banyak klinik menampung pasien yang terpapar virus tersebut. Dari kasus tersebut, rekomendasi penentuan klinik sangat diperlukan karena kondisi yang sangat darurat dan kasus positif yang bertambah setiap harinya. Pada penelitian ini, ditambahkan metode information retrieval, yaitu metode TF-IDF dan BM25 untuk menentukan rekomendasi klinik di Kabupaten Jember berdasarkan kata pencarian dari penggunanya dan diurutkan berdasarkan kemiripan (similarity) dari yang terbesar hingga yang terkecil. Sementara metode Haversine Formula digunakan untuk memilih klinik dengan jarak yang ditentukan oleh pengguna sebelumnya Penentuan rekomendasi klinik yang menggunakan metode gabungan information retrieval (similarity) + haversine dilakukan dengan formulasi rata-rata peringkat antara metode haversine dengan metode gabungan, dan formulasi normalisasi nilai similarity maupun nilai haversine. Hasilnya, ada 7 klinik yang menempati peringkat terbaik untuk metode gabungan dengan formulasi rata-rata peringkat, dan ada 47 klinik yang menempati peringkat terbaik untuk metode gabungan dengan formulasi normalisasi.
Information Technology Governance Analysis to Reduce Information Security Risks Using Cobit 2019: A Case Study of Manufacturing Companies Nugroho, Aditia; Ginardi, Hari
Jurnal Indonesia Sosial Teknologi Vol. 5 No. 8 (2024): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v5i8.1198

Abstract

PT Krakatau Steel (Persero) Tbk is a company engaged in the manufacturing industry that utilizes digital transformation to improve efficiency, optimize facilities and assist organizations in making business decisions quickly. However, there are new challenges in implementing digital transformation, namely increasing dependence on information technology (IT) and triggering high potential threats to information security. Therefore, good information technology governance is needed in managing information security. The research method is based on the Control Objectives for Information Technologies (COBIT) framework version 2019 as the best guide in managing information technology governance. The research was conducted in several stages, including data collection through observation of policy documents and interviews with employees of the BEICT (Business Enables & Information Communication Technology) Department who are responsible for managing services and maintaining the company's digital assets. Evaluation of the maturity level was carried out on 8 priority objectives consisting of EDM03, EDM05, APO12, APO13, APO14, BAI09, DSS05, and MEA04 based on design factor assessment. The results of the analysis of selected domain activities, the IT governance maturity level was at 2.56 (managed level). Indicates that the organization has managed and implemented information security activities, but some activities do not yet have written policies or procedures. With recommendations in the form of proposed improvements to the aspects of people, processes and technology, it is hoped that it can increase the level of maturity of IT governance in reducing information security risks and supporting digital transformation programs.
Co-Authors Ahkam, Syuaib Ahmad Saikhu Ahmad Yusuf Ali Sofyan Kholimi Aprilio, Adrianus Yoza Arifiani, Siska Aunurohim Aunurohim Bilqis Amaliah Briantoro, Hendi Chastine Fatichah Ciptaningtyas, Henning Dewi, Ratih Kartika Diema Hernyka Satyareni Eka Sari Oktarina Evy Kamilah Ratnasari Fahmi Akbar Saputra Fauzi, Haffif Rasya Fraditya, Awang Hanifah, Asiyah hidayat, dwi taufik Hisyam, Achmad Aushaf Amrega Hizham, Fadhel Akhmad indrawanti, annisaa sri Isye Arieshanti Kusuma, Zulfa Hafizh Luqmanulhakim, Naufan Zaki Machfud, M. Mughniy Manurung, Miranda Maranatha Wijayaningtyas Mohammad Yanuar Hariyawan Mohammad, Iki Adfi Nur Muchammad Husni Mustika Mentari Nanik Suciati Noor Fitria Azzahra Nugroho, Aditia Parulian, Boy Pasya, Muhammad Naufal Pradigdya, Christian Andrean Pratiwi, Adinda Putri Putri, Rachmah Agus Ratih Kartika Dewi Ridho Rahman Hariadi Rizka Wakhidatus Sholikah, Rizka Wakhidatus Sabilla, Irzal Ahmad Salsabilla, Rehana Putri Santosa, Hafis Akmaldi Santoso, Dian Septiani Siti Mutrofin Suastika Yulia Riska Surya, Maulidina Rahmawati Susijanto Tri Rasmana Syafa, Ilhan Ahmad Syahputra, Muhammad Harvian Dito Wicaksono, M. Januar Eko Wicaksono, Rahmad Aji Wijaya, Agas Ananta