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All Journal Teknotan: Jurnal Industri Teknologi Pertanian Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Edu Komputika Journal Jurnal Teknologi dan Sistem Komputer International Journal of Artificial Intelligence Research Jurnal Elektronika dan Telekomunikasi Jurnal Teknologi Informasi dan Multimedia Jurnal Abdi Insani Jurnal Bumigora Information Technology (BITe) Journal of Applied Data Sciences Journal of Community Development Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Bakti Sekawan : Jurnal Pengabdian Masyarakat Paradharma: Jurnal Aplikasi IPTEK Jurnal INFOTEL International Journal Research on Metaverse
Syahroni Hidayat
Universitas Negeri Semarang
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Nursing Public Health Social Sciences Other

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Ensemble Implementation for Predicting Student Graduation with Classification Algorithm Ria Rismayati; Ismarmiaty Ismarmiaty; Syahroni Hidayat
International Journal of Engineering and Computer Science Applications (IJECSA) Vol 1 No 1 (2022): March 2022
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (420.941 KB) | DOI: 10.30812/ijecsa.v1i1.1805

Abstract

Graduating on time at the higher education level is one of the main targets of every student and university institution. Many factors can affect a student's length of study, the different character of each student is also an internal factor that affects their study period. These characters are used in this study to classify data groups of students who graduated on time or not. Classification was chosen because it is able to find a model or pattern that can describe and distinguish classes in a dataset. This research method uses the esemble learning method which aims to see student graduation predictions using a dataset from Kaggle, the data used is a IPK dataset collected from a university in Indonesia which consists of 1687 records and 5 attributes where this dataset is not balanced. The intended target is whether the student is predicted to graduate on time or not. The method proposed in this study is Ensemble Learning Different Contribution Sampling (DCS) and the algorithms used include Logistic Regression, Decision Tree Classifier, Gaussian, Random Forest Classifier, Ada Bost Classifier, Support Vector Coefficient, KNeighbors Classifier and MLP Classifier. From each classification algorithm used, the test value and accuracy are calculated which are then compared between the algorithms. Based on the results of research that has been carried out, it is concluded that the best accuracy results are owned by the MLPClassifier algorithm with the ability to predict student graduation on time of 91.87%. The classification model provided by the DCS-LCA used does not give better results than the basic classifier of its constituent, namely the MLPClassifier algorithm of 91.87%, SVC of 91.64%, Logistic Regression of 91.46%, AdaBost Classifier of 90.87%, Random Forest Classifier of 90.45% , and KNN of 89.80%.
Peningkatan Kompetensi Penulisan Artikel Ilmiah Kepada Guru-Guru Di Wilayah Kabupaten Lombok Barat Jihadil Qudsi; Andi Sofyan Anas; Akbar Juliansyah; Adam Bachtiar Maulachela; Raden Fanny Printi Ardi; Syahroni Hidayat; Danang Tejo Kumoro; Uswatun Hasanah; Sandi Justitia Putra
Bakti Sekawan : Jurnal Pengabdian Masyarakat Vol 1 No 1 (2021): Juni
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (196.143 KB) | DOI: 10.35746/bakwan.v1i1.149

Abstract

One of the consequences of teachers as functional positions is that teachers are required to carry out continuous professional development (CPD) so that teachers can carry out their duties and functions professionally. For this reason, teachers are required to have competency in researching and writing scientific papers in the form of books, modules and scientific articles. Writing scientific papers for teachers can serve as a reference / reference to increase insight or disseminate knowledge. This community service aims to equip teachers with scientific writing material, especially on scientific articles as well as provide assistance and consultation in writing scientific papers so that teachers are able to make scientific papers properly and correctly. The target of this service is partner schools from fellow institutes in West Lombok district, where the teachers who serve the school are expected to be able to produce a publication in a scientific paper.
EVALUASI SISTEM INFORMASI PENGGUNAAN E-LEARNING SEBAGAI SISTEM PERKULIAHAN PERGURUAN TINGGI Uswatun Hasanah; Syahroni Hidayat; Danang Tejo Kumoro
JURNAL INFOTEL Vol 12 No 4 (2020): November 2020
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v12i4.475

Abstract

This study aims to evaluate the use of technology to support teaching and learning activities. Lecturers and students have applied e-learning to teach subjects. The purpose of this evaluation is to measure the success of the use of STMIK Bumigora e-learning by using the Technology Acceptance Model (TAM) approach, which is an approach that can explain user behavior towards the use of technology. Evaluation of the use of e-learning is formulated into a model based on the TAM model, while SEM (Structural Equation Modelling) is used for data analysis. Based on the measurement analysis in this study, several factors most influenced the effectiveness of e-learning, namely the usage tutorial for users, ICT facilities related to the Ease of accessing the internet network. Meanwhile, in structural analysis, it was found that attitudes toward the use and perceived usefulness were strongly correlated with real use factors. The actual use is a real condition of the use of e-learning measured by the frequency and duration of time in using the technology, which is influenced by the user's belief in accepting the existence of e-learning in STMIK Bumigora and user beliefs related to the benefits when using it. Therefore, attitudes toward the use and perception of usefulness are the main determining factors in measuring the frequency and duration of e-learning use.
IMPLEMENTASI PERTANIAN CERDAS BERBASIS IOT PADA KELOMPOK TANI TEGER 02 DESA MANGUNSARI: Implementation of IoT-Based Smart Farming in the TEGER 02 Farmer Group in Mangunsari Village Anan Nugroho; Feddy Setio Pribadi; Mona Subagja; Syahroni Hidayat; Ahmad Zein Al Wafi; Muhammad Fathurrahman; Zidan Vieri Wijaya; Agus Ardiyanto; Haikal Abror
Jurnal Abdi Insani Vol 10 No 4 (2023): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v10i4.1267

Abstract

Plantation is a very important sector to meet food needs in Indonesia. However, plantations in Indonesia do not always increase over time. There are still many plantation sectors whose productivity is still low, especially in rural areas. Factors such as lack of capital and technology, lack of market access, and social problems such as land conflicts often become obstacles to the development of a productive and sustainable plantation sector. One example of a farmer group experiencing problems in water distribution is the TEGER 02 farmer group in Mangunsari Village, Semarang City, Central Java. This group has 7 hectares of land and 20 workers. However, the biggest obstacle faced is ineffective water distribution, especially during the dry season. Currently, to water 3000 𝑚2 of cultivated land, the group needs a full day involving 3 workers. Of course, the ratio between a few workers and a large area of land is inversely proportional, so it takes workers a long time to water the plantation land. To overcome this problem, a tool has been developed that can help farmers distribute water that utilizes strong internet access, the Internet of Things (IoT). IoT is very suitable to be applied to Mangunsari Village plantations. An IoT-based sprinkler that can help farmers in watering automatically which is connected to a website application so that it can be accessed directly using the farmer's smartphone. As an electricity supply, photovoltaics are used as an environmentally friendly energy source with a conservation perspective. The results of the service show that this activity makes it very easy for farmers to overcome watering problems which are difficult to control because the number of workers is not proportional to the size of the plantation land. However, adjustments to the website need to be made to produce an application that is more responsive when accessed via farmers' smartphones.
Design of Brushless DC Motor Driver Based on Bootstrap Circuit Fathoni, Khoirudin; Apriaskar, Esa; Salim, Nur Azis; Sulistyawan, Vera Noviana; Satria, Rifki Lukman; Hidayat, Syahroni
Jurnal Elektronika dan Telekomunikasi Vol 23, No 2 (2023)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.563

Abstract

Brushless DC (BLDC) motor is a three-phase motor that cannot work directly with DC current but requires electronic commutation to replace the brush function in DC motor. This paper aims to implement BLDC motor driver integration based on bootstrap circuit using Autodesk Eagle. The driver board consist of bootstrap circuit based on IR2110, MOSFETs, three voltage regulator, ESP32 microcontroller and ACS712 current sensor connection, logic level converter, and BLDC hall effect signal sensor conditioning. The research proposes bootstrap capacitor calculation based on charging/discharging capacitor principle and the minimum motor speed rotation. The implemented driver has 14x10 cm dimension tested to drive 24V/135W/6000rpm sensored BLDC motor using six steps commutation with pulse width modulation (PWM) inserted programmatically in ESP 32 to drive the high side MOSFET of the driver without AND gate circuit. The effect of pwm frequency and dutycycle variation to the speed and current of the motor is investigated. The results showed that the driver with both 12 V and 24 V voltage source and 68 μF bootstrap capacitor work optimally in 20 KHz PWM frequency both in open loop and closed loop speed control test. The motor reach 129 W for the largest power and 5250 rpm for the fastest speed in 24 V supply.
Comparison of Ensemble Learning Methods for Mining the Implementation of the 7 Ps Marketing Mix on TripAdvisor Restaurant Customer Review Data Sunarko, Budi; Hasanah, Uswatun; Hidayat, Syahroni
International Journal of Artificial Intelligence Research Vol 7, No 2 (2023): December 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i2.1096

Abstract

The 7P marketing mix encompasses various business facets, notably the Process element governing internal operations from production to customer service. With the surge in online customer feedback, assessing machine learning efficacy, especially ensemble learning, in classifying 7P-related customer review data has gained prominence. This research aims to fill a gap in existing literature by evaluating ensemble learning’s performance on 7P classification, an area not extensively explored despite prior sentiment analysis studies. Employing a methodology merging Natural Language Processing (NLP) with ensemble learning, the study processes restaurant reviews using NLP techniques and employs ensemble learning for precision and accuracy. Findings demonstrate that DESMI yielded the highest performance metrics with accuracy at 0.697, precision at 0.699, recall at 0.697, and an F1-score of 0.684. These outcomes underscore ensemble learning's potential in handling complex datasets, signifying its relevance for marketers and researchers seeking comprehensive insights from customer reviews within the 7P marketing mix domain. This study sheds light on how ensemble learning outperforms its foundational methods, indicating its prowess in extracting meaningful insights from diverse and intricate customer feedback.
Penerapan Stacking Ensemble Learning untuk Klasifikasi Efek Kesehatan Akibat Pencemaran Udara Sunarko, Budi; Hasanah, Uswatun; Hidayat, Syahroni; Muhammad, Naufal; Ardiansyah, Muhammad Irfan; Ananda, Briska Putra; Hakiki, Muhammad Khikam; Baroroh, Luluk Taufiqul
Edu Komputika Journal Vol 10 No 1 (2023): Edu Komputika Journal
Publisher : Jurusan Teknik Elektro Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukomputika.v10i1.72080

Abstract

Pencemaran udara merupakan masalah serius yang berdampak negatif pada kesehatan manusia. Berbagai jenis polutan udara seperti partikel halus, sulfur dioksida, nitrogen oksida, dan ozon dapat menyebabkan gangguan pernapasan, penyakit jantung, kanker paru-paru, dan masalah kesehatan lainnya. Untuk memahami dampak kesehatan pencemaran udara, klasifikasi efek kesehatan akibat pencemaran udara menjadi penting. Metode klasifikasi ini membagi efek kesehatan berdasarkan jenis polutan, dosis, dan waktu paparan. Penelitian ini mengusulkan penerapan metode klasifikasi dengan ensemble learning untuk mengidentifikasi polutan berdampak dan tingkat risiko kesehatannya. Ensemble learning adalah teknik pembelajaran mesin yang menggabungkan beberapa model untuk meningkatkan akurasi prediksi. Stacking ensemble learning merupakan salah satu metode yang digunakan dalam klasifikasi efek kesehatan pencemaran udara dengan mengintegrasikan beberapa model dasar seperti Logistic Regression, Decision Tree, K-Nearest Neighbor, Support Vector Machine, dan Multi-Layer Perceptron. Hasil penelitian menunjukkan bahwa model Stacking memberikan performa tertinggi dengan akurasi sekitar 99,9% pada dataset baik yang seimbang maupun tidak seimbang. Namun, model Decision Tree dan K-Nearest Neighbor juga berhasil memberikan performa yang sangat baik. Waktu pelatihan model menjadi pertimbangan penting, di mana K-Nearest Neighbor dan Decision Tree memiliki waktu yang jauh lebih singkat dibandingkan dengan model Stacking.
Implementasi SMARCOS: Smart Water Conditioning System Berbasis Web-IoT di Balai Benih Ikan Kecamatan Mijen Semarang Nugroho, Anan; Subagja, Mona; Hidayat, Syahroni; Budiwirawan, Agung; Diyanasari, Ledi; Simanjuntak, Jhonatur Stheven; Wahyudi, Tri Agus; Fikri, Akmal
Journal of Community Development Vol. 6 No. 1 (2025): August
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/comdev.v6i1.1459

Abstract

Effective water quality management is crucial for fish hatcheries to ensure survival and productivity. At the Fish Hatchery Center (BBI) Cangkiran Mijen, water quality monitoring is still conducted manually, leading to unstable pond conditions. To improve monitoring efficiency, SMARCOS (Smart Water Conditioning System) was developed as a Web-IoT-based system for automated monitoring of water parameters such as pH, oxygen, and temperature. The program involved pond data collection, expert consultation, system design, testing, implementation, and partner training. Evaluation was conducted through satisfaction surveys and system performance monitoring. Results showed that SMARCOS effectively corrected water quality parameters automatically, enhanced monitoring efficiency, and provided easy access to information via an IoT-based website. Surveys indicated that partners were satisfied with the system’s usability. The adoption of IoT for water quality monitoring significantly improved the efficiency and accuracy of hatchery pond management. Training sessions also increased partner understanding of IoT technology. The success of SMARCOS demonstrates that IoT can be an innovative solution for fisheries modernization, with potential replication in other hatcheries to enhance productivity and efficiency in aquaculture.
Integration of Sentiment Analysis and RFM in Restaurant Customer Segmentation: A 7P-Based CRM Model with Clustering Sunarko, Budi; Hasanah, Uswatun; Hidayat, Syahroni; Rachmawati, Rina
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.633

Abstract

The increasing use of digital platforms like Tripadvisor has created opportunities to transform customer review data into strategic insights for Customer Relationship Management (CRM). This study proposes a novel CRM model by integrating the Recency, Frequency, Monetary (RFM) framework with the 7P marketing mix to segment restaurant customers more effectively. Using 3,716 Tripadvisor reviews, annotated based on 7P elements and clustered through unsupervised learning, three key customer segments were identified: acquisition, retention, and win-back. Evaluation metrics show strong clustering performance with a Silhouette Score of 0.73 and a Davies-Bouldin Score of 0.08. The acquisition cluster (Product) demonstrates the highest Frequency (37,664) and Monetary value (64.94), signifying high engagement and revenue potential. The retention cluster (Physical Evidence, Place, Process, Promotion, Traveler) shows stable interaction patterns with Recency values of 1261–1262 and moderate Frequency (378–2,079). The win-back cluster (Price, People) reflects lower Frequency (198–946) but equal Recency (1259), indicating recent but infrequent activity, which is ideal for reactivation strategies. By mapping customer reviews to 7P labels and analyzing them using RFM, the model uncovers specific behavioral patterns tied to service quality, pricing, and promotions. This integration allows restaurants to apply tailored strategies: offering loyalty rewards to high-frequency customers, promotional incentives for those with high Recency, and prioritizing high-monetary customers for exclusive programs. The novelty of this research lies in its combined use of sentiment-based review analysis and RFM–7P segmentation, offering a scalable, data-driven framework for enhancing customer satisfaction, loyalty, and long-term business growth in the restaurant industry.
Penentuan Filterbank Wavelet Menggunakan Algoritma Mean Best Basis untuk Ekstraksi Ciri Sinyal Suara Ber-Noise Abdurahim, Abdurahim; Hidayat, Syahroni
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 1: Februari 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

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

Abstract

Belakangan ini filterbank berbasis wavelet sebagai ekstraktor ciri mulai banyak dikembangkan untuk dapat menggantikan peran ciri Mel Frequency Cepstral Coefficient (MFCC) dalam sistem pengenalan suara otomatis. Salah satu filterbank ciri wavelet yang dikembangkan adalah Wavelet-Packet Cepstral Coefficient (WPCC). Namun sejauh ini pengembangannya hanya difokuskan untuk suara tanpa noise. Sehingga penelitian ini bertujuan untuk mendesain WPCC untuk suara yang mengandung noise. Algoritma Mean Best Basis (MBB) dan fungsi wavelet db44 dan db45 digunakan untuk memperoleh desain filterbank WPCC. Suara yang digunakan adalah rekaman suara vokal bahasa Indonesia a, i, u, e, é, o, dan ó yang mengandung noise. Hasil menunjukkan telah terbentuk dua buah desain filterbank WPCC. Masing-masing merupakan hasil penerapan fungsi daubechies db44 dan db45. Noise tidak memberikan pengaruh terhadap pembentukan kedua filterbank WPCC tersebut. Kedua bentuk filterbank telah memenuhi standar bentuk filter MFCC terutama untuk variabel range dan skala frekuensinya. Range frekuensinya berkisar antara 125 Hz - 1000 Hz dengan bentuk skala yang linier untuk frekuensi di bawah 1000 Hz. Sehingga dapat disimpulkan kedua bentuk filterbank WPCC ini dapat dipertimbangkan untuk digunakan sebagai ekstraktor ciri suara ber-noise. AbstractRecently wavelet-based filterbanks as feature start extractors have been widely developed to replace the role of the Mel Frequency Cepstral Coefficient (MFCC) feature in automatic speech recognition systems. One of the wavelet feature filterbanks developed is Wavelet-Packet Cepstral Coefficient (WPCC). But so far the development has only been focused on clean speech signal. So, the aim of this study is designing WPCC for a noisy speech signal. The Mean Best Basis (MBB) algorithm and db44 and db45 wavelet functions are applied to obtain the WPCC filterbank design. The noisy speech signal used is the recorded utterance Indonesian vowels a, i, u, e, é, o, and ó. The results show that two WPCC filterbank designs have been formed. Each of them is the result of applying the daubechies db44 and db45 functions. Noise has no effect on the establishment of both the WPCC filterbanks. Both fiterbank designs have met MFCC filter form standards, especially for its range of frequency and frequency scale. Its range of frequency is between 125 Hz - 1000 Hz with a linear scale for frequencies below 1000 Hz. Therefore it can be concluded that the two forms of WPCC filterbank can be considered to be used as a feature extractor for a noisy speech signal.
Co-Authors Abdulloh Abdulloh Abdurahim, Abdurahim Adam Bachtiar Maulachela Agung Budiwirawan Agus Ardiyanto Ahmad Zuli Amrullah Ahmat Adil Akbar Juliansyah Anan Nugroho Ananda, Briska Putra Andi Sofyan Anas Ansar Ansar Ardiansyah, Muhammad Irfan Baroroh, Luluk Taufiqul Budi Sunarko Budiarto, Jian Danang Tejo Kumoro Danang Tejo Kumoro Dian Syafitri Chani Saputri Diyanasari, Ledi Esa Apriaskar Feddy Setio Pribadi Fikri, Akmal Habib Ratu Perwira Negara Haikal Abror Hakiki, Muhammad Khikam Hanif Ardhiansyah Hanif Hidayat Intan Ermawati Ismarmiaty Ismarmiaty, Ismarmiaty Joelianto Darmawan Joko Sumarsono Khoirudin Fathoni, Khoirudin Kumoro, Danang Tejo Mona Subagja Muhammad Fathurrahman Muhammad Hilmy Herdiansyah Muhammad Imam Dinata Muhammad Muhammad MUHAMMAD TAJUDDIN Muhammad, Naufal Murad Murad Murad, Murad Nani Sulistianingsih Ni Luh Putu Merawati Nugroho, Anan Nur Iksan Qudsi, Jihadil R Fanny Priniti Raden Fanny Printi Ardi Rahmat Sabani Rezky Ramdhaningsih Ria Rismayati Rian Febriyanto Rina Rachmawati Risanuri Hidayat Rismayati, Ria Rizal, Ahmad Ashril Salim, Nur Azis Sandi Justitia Putra Satria, Rifki Lukman Simanjuntak, Jhonatur Stheven Sitti Agripina Alodia Yusuf Subagja, Mona Sukmawaty Sukmawaty Sukmawaty Sukmawaty Sulistyawan, Vera Noviana Tajuddin, Muhammad Taofan Ali Achmadi Teguh Bharata Adji Uswatun Hasanah Uswatun Hasanah USWATUN HASANAH Wafi, Ahmad Zein Al Wahyudi, Tri Agus Yusuf, Siti Agrippina Alodia Zaenal Abidin Zaurarista Dyarbirru Zidan Vieri Wijaya