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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi Karya Ilmiah S.1 Ilmu Keperawatan Business and Finance Journal Jurnal Tekinkom (Teknik Informasi dan Komputer) Journal Research of Social Science, Economics, and Management Sewagati
Agus Budi Raharjo
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FAKTOR-FAKTOR YANG MEMPENGARUHI KEKAMBUHAN PADA PASIEN SKIZOFRENIA DI RSJD dr.AMINO GONDOHUTOMO SEMARANG Raharjo, Agus Budi; rochmawati, dwi heppy; -, purnomo
Karya Ilmiah S.1 Ilmu Keperawatan Tahun 2014
Publisher : STIKES Telogorejo Semarang

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

Abstract

Faktor yang memicu kekambuhan skizofrenia, antara lain penderita tidak minum obat dan tidak kontrol ke dokter secara teratur, menghentikan sendiri obat tanpa persetujuan dokter, kurangnya dukungan dari keluarga dan masyarakat, serta adanya masalah kehidupan yang berat dapat memicu stress. Tujuan dalam penelitian ini adalah untuk mengetahui faktor-faktor yang mempengaruhi kekambuhan pada pasien skizofrenia di RSJD dr. Amino Gondohutomo Semarang. Desain penelitian ini menggunakan analitik korelasional dengan pendekatan cross sectional. Sampel yang digunakan sebanyak 19 responden dengan teknik purposif sampling. Pengumpulan data menggunakan kuesioner yang akan di isi oleh keluarga pasien. Koesioner akan di uji kode etik (uji validitas data) terlebih dahulu sebelum digunakan untuk mengumpulkan data responden, setelah uji etik memenuhi syarat maka koesioner dapat digunakan sebagai alat penelitian. Data diolah untuk memperoleh distribusi frekuensi dan masing-masing data di uji statistik untuk melihat adanya hubungan antara kedua variabel, menggunakan uji lamda dengan nilai p=0,000 (p ≤ 0,05) maka ada hubungan antara kepatuhan minum obat,keteraturan kontrol dokter, dukungan keluarga dan dukungan sosial dengan frekuensi kekambuhan pada pasien skizofrenia di RSJD dr. Amino Gondohutomo Semarang. Penelitian ini diharapkan dapat dijadikan sebagai tambahan pengetahuan untuk mengurangi frekuensi kekambuhan pasien skizofrenia, menambah pengetahuan dan wawasan perawat khususnya mengenai faktor-faktor yang mempengaruhi kekambuhan pada pasien skizofrenia.Kata Kunci : Kekambuhan, Skizofrenia
PENGGABUNGAN KEPUTUSAN PADA KLASIFIKASI MULTI-LABEL Raharjo, Agus Budi; Quafafou, Mohamed
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 13, No 1, Januari 2015
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v13i1.a384

Abstract

Klasifikasi adalah bagian dari sistem pembelajar yang fokus pada pemahaman pola melalui representasi dan generalisasi data. Penentuan prediksi hasil klasifikasi terbaik menjadi masalah jika terdapat beberapa masukan dari metode yang berbeda-beda pada lingkungan data yang heterogen. Penggabungan keputusan dapat digunakan untuk menentukan rekomendasi keluaran beberapa metode klasifikasi. Kami memilih pendekatan voting dan meta-learning sebagai metode penggabungan keputusan. Ada dua fase yang dilakukan pada penelitian ini, yaitu fase pembangunan prediksi oleh metode klasifikasi yang heterogen dan fase penggabungan rekomendasi metode-metode tersebut menjadi satu kesimpulan jawaban. Karakteristik klasifikasi yang menjadi fokus adalah klasifikasi multi-label. Binary Relevance (BR), Classifier Chains (CC), Hierarchichal of Multi-label Classifier (HOMER), dan Multi-label k Nearest Neighbors (MLkNN) adalah metode klasifikasi yang digunakan sebagai penyedia rekomendasi prediksi melalui pendekatan yang berbeda-beda. Pada fase penggabungan keputusan, metode Ignore diajukan sebagai pendekatan meta-learning. Ignore menggabungkan keputusan dengan cara mempelajari pola masukan dari sistem pembelajar. Untuk membandingkan kinerja Ignore, metode konsensus digunakan sebagai pendekatan voting. Hasil akhir menunjukkan bahwa Ignore memberikan hasil terbaik untuk parameter recall. Ignore memprediksi nilai false negative lebih sedikit dibandingkan dengan metode konsensus 0,5 dan 0,75. Hasil studi ini menunjukkan bahwa Ignore dapat digunakan sebagai meta-learning, meskipun kinerja Ignore harus diperbaiki agar dapat beradaptasi dengan data yang heterogen.
Strategi Pengenalan Pemrograman Web di SMP Al-Hikmah Surabaya: Pendekatan Inovatif untuk Pendidikan Digital Raharjo, Agus Budi; Maheswari, Clarissa Luna; Purwitasari, Diana; Sunaryono, Dwi; Baskoro, Fajar
Sewagati Vol 8 No 4 (2024)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v8i4.1057

Abstract

Di tengah perkembangan teknologi yang semakin canggih, keterampilan pemrograman web menjadi aset yang berharga, terutama bagi generasi muda yang sedang menyiapkan diri untuk era digital. Pengabdian masyarakat ini berisi kegiatan pelatihan pemrograman web di SMP Al-Hikmah Surabaya, dengan tujuan untuk menanamkan dasar-dasar pemrograman kepada siswa dan mengintegrasikan keahlian ini dalam kurikulum sekolah menengah. Mengadaptasi silabus Departemen Teknik Informatika Institut Teknologi Sepuluh Nopember Surabaya dan beragam sumber literatur, program pelatihan ini dirancang untuk memberikan pengenalan kepada HTML, CSS, JavaScript, dan kerangka kerja Bootstrap. Pelatihan ini melibatkan mahasiswa dan dosen dari Departemen Teknik Informatika yang berkolaborasi dengan ekstrakurikuler pemrograman di SMP Al-Hikmah. Dengan pendekatan interaktif, praktis, dan kolaboratif, kegiatan ini telah meningkatkan pemahaman teknologi informasi di kalangan siswa, mendorong kreativitas, serta memperkuat persiapan para siswa untuk pendidikan lanjutan dan tantangan masa depan. Inisiatif ini juga menargetkan keluaran dalam bentuk publikasi ilmiah dan materi pelatihan yang dapat diakses oleh publik, menandai kontribusi berkelanjutan terhadap pengembangan pendidikan digital di Indonesia.
ANALYSIS OF CUSTOMER PROFILE CHARASTERISTIC WITH CREDIT QUALITY USING THE CLUSTERING METHOD FOR RISK MITIGATION AND SMALL MEDIUM ENTERPRISE CREDIT PORTOFOLIO EXPANSION PLANNING Yorinda, Ilham Achmadi; Raharjo, Agus Budi
Jurnal Bisnis dan Keuangan Vol 8 No 2 (2023): Business and Finance Journal
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/bfj.v8i2.5225

Abstract

in managing the Small Medium Enterprise credit portfolio. The strategy taken by a bank must be adapted to the general characteristics of the target debtors for business expansion and risk mitigation so that business expansion and risk mitigation efforts can be carried out effectively for certain groups. Based on these problems, the purpose of this research is to identify groups of debtors with similar characteristics based on debtor profile data and their credit quality, and to understand the differences in credit risk and opportunities for business expansion among these groups. The data used is the profile of the distribution of debtors from Bank ABC of 5088 debtors. The analysis technique used is K-Means and KMedoids with the evaluation criteria used are silhouette score, davies-bouldin index and computation time. Completion of the optimal number of groups is done by analyzing the WSS graph using the elbow method. Analysis of business expansion and risk reduction is carried out separately where business expansion analysis is carried out for debtors with a collectibility value of 1 and risk reduction analysis is carried out for debtors who have a collectibility value of 2 – 5. The results show that there are 5 groups in the business expansion analysis and 3 groups in risk mitigation analysis. The high value of the silhouette index and davies-bouldin index makes the grouping results have a strong structure. The KMedoids method is used in this analysis because it has better evaluation criteria than K-Means. Priority is also determined for each group formed so that it can be determined which group has the greatest opportunity to become target expansion and which group has the greatest risk that needs to be mitigated. In general, business sectors such as agriculture and plantations are experiencing a decline in economic activity in 2022, so it needs attention from the bank so that it does not disrupt the credit portfolio. To complement the results of this study, an analysis of external and internal business health needs to be carried out in more depth so that the big picture of credit problems at Bank ABC can be identified.
IMPLEMENTASI SISTEM ANTRIAN ONLINE PADA DUKCAPIL KLATEN MENGGUNAKAN METODE USER CENTERED DESIGN (UCD) Raharjo, Agus Budi; Aji, Adam Sekti
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1748

Abstract

The Population and Civil Registration Office (Dukcapil) has an important role in providing public services related to the processing of population documents. In Klaten Regency, people often face problems in taking care of legal identity documents at the Dukcapil, such as long and disorganized queues. The queuing system that is still done manually is considered less efficient and prone to fraud. The community also has difficulty in monitoring the order of the queue, so they have to wait a long time without certainty when they will be served. To overcome these problems, an integrated and computerized online queue information system is needed. This research aims to design and build an online queue information system at the Klaten Regency Dukcapil using the User Centered Design method. This system can provide better, efficient, and transparent services to the community. System development uses the Kotlin programming language and MySQL database. Black box testing is also carried out to find out the errors that exist in the system. Through the online queuing system, people can take queue numbers online and monitor the queue sequence in real time, thus providing convenience and comfort in accessing Dukcapil services. Thus, the quality of public services, especially in the processing of population documents, can be improved.
DESIGN OF I-SLA (ISLAMIC LEARNING APPLICATION) AS TAJWEED LEARNING MEDIA BY USING THE SPEECH RECOGNITION TECHNOLOGY Hariadi, Ridho Rahman; Arifiani, Siska; Raharjo, Agus Budi; Fabroyir, Hadziq
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 21, No. 1, January 2023
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v21i1.a1121

Abstract

Indonesia is a country with the majority of the population converting to Islam, which is more than 87% of the total population of Indonesia. As Muslims who adhere to the teachings of Islam, the teachings that must be understood are tajweed lessons. Tajweed science is the science that studies how to read the Qur'an properly and correctly. Adherents of Islam in Indonesia are still many who do not understand and cannot read the Qur'an properly and correctly. Research noted that there are still about 65% of Indonesian Muslims still blind to the writings of the Qur'an. The importance of learning tajweed science is that it can read precisely, if there are errors in reading the Quran can change its true meaning. Tajweed lessons are commonly obtained through non-formal educational institutions that focus on learning Islam. The current pandemic period causes all learning activities to be limited and difficult, including learning al quran education.  Online learning applications today are still rare that develop Quran Education including tajweed science, so people who want to learn the science have not found the right tools. We are planning an application called I-SLA (Islamic learning application). I-SLA is a tajweed learning application that utilizes speech recognition to correct the pronunciation of the user's Quran and provide justification if in pronunciation there is still something wrong, this technology has the ability to exchange information using acoustic signals. In addition, there is a consulting feature of tajweed experts if they feel they want to deepen tajweed knowledge. The design of the application in this study was carried out in a direct manner. The mechanism of this research is made by conducting a literature study for the process of making software needs specifications, followed by the creation of software design with UI / UX, followed by the creation of applications and closed with testing. This process is carried out continuously in accordance with the planning period. The result of this study is the application of I-SLA (Islamic Learning Application) with the aim of users of children, adolescents, and adults who want to deepen tajweed science to improve its pronunciation.
K-MEANS AND XGBOOST FOR CUSTOMER ELECTRICITY ACCOUNT PAYMENT BEHAVIOR ANALYSIS (CASE STUDY: PLN ULP PANAKKUKANG) Nugraha, Raditya Hari; Purwitasari, Diana; Raharjo, Agus Budi
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 20, No. 2, July 2022
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v20i2.a1132

Abstract

Revenue Acceleration from electricity account receivables is one of the energy companies' efforts to maintain cash flow so that they can carry out operational activities and carry out investment activities to develop company assets. Factors that influence electricity bill payment behavior include the location of consumers, the amount of the bill, payment point facilities located around consumers' homes, the use of digital technology as a media of payment, as well as consumer awareness and understanding regarding the time limit for paying electricity bills. Therefore, it is necessary to conduct an analysis so that the company can determine a special strategy for customers who have the potential to be in arrears in electricity bills. To get the characteristic of electricity bill payments, several previous studies have used various classification methods of machine learning such as random forest, nave bayes, SVM, CART, etc. to get the best accuracy. In this research, to increase the accuracy of the model, author using the cluster method with the k-means technique and combining it with the eXtreme Gradient Boosting (XGBOOST) classification method based on data on the characteristics of consumer electricity bill payments. In this study also used hyperparameter adjustment with hillclimbing, random search, and bayesian techniques to increase the accuracy of the model. The model simulation carried out in this thesis gives the result that the combination of the k-means cluster with the XGBoost classification and by adjusting the bayesian technique hyperparameters has a much better model accuracy rate with a value of 89.27% and an Area Under Curve (AUC) value of 0.92 when compared to gradient boosting method with an accuracy rate of only 74.76% and an AUC value of 0.75. Based on the simulation results on ULP Panakkukang customer data, it was found that the subsidy category customer group and customers who often experience power outages have a tendency to be in arrears on electricity bills.
LOAD FORECASTING FOR DAILY LOAD OPERATIONAL PLAN USING LSTM (CASE STUDY: SOUTH SULAWESI SUB SYSTEM) Raharjo, Agus Budi; Wakhid, Muhammad Abdul; Purwitasari, Diana
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 20, No. 2, July 2022
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v20i2.a1138

Abstract

The electrical load required in an electricity sub-system changes every day. Electric power operators must be able to generate and distribute electricity according to consumer needs. In the Sulawesi sub-system, the power plants used are still dominated by fossil fuel generators, so that in their operations, fuel requirements need to be given serious attention. Planning a good daily electricity consumption is needed so that the fuel cost becomes optimal. In the current condition, the load forecasting for the Daily Load Operation Plan (ROH) is still based on Expert Judgment, which is different for each forecaster. With a fairly large error tolerance limit of 4%. We need a load forecasting instrument capable of better error tolerance. Forecasting methods such as ARIMA, SARIMA and ARIMAX have been used for many years. In recent years, several artificial intelligence techniques such as Neural Network and machine learning have been developed for time series analysis. And recently, more accurate forecasting results are shown by Artificial Neural Network (ANN) and Recurrent Neural Network (RNN) compared to traditional forecasting methods. Long Short Term Memory (LSTM) is a model of RNN that uses past data (Long Term) to predict current data (Short Term). Electric load in Sulawesi subsystem used as data training after normalized using min-max normalization. The LSTM model is made with different data input. Forecasting  performance of each model is then evaluated based on the RMSE and MAPE values. Of the several data input models, forecasting models with daily data input show better performance than other scenarios. The MAPE and RMSE values obtained were 2.384% and 33.95, respectively.
Development of a transformer asset management dashboard in upstream oil and gas companies through ABC and FSN analysis Susanti, Lestari Indah; Raharjo, Agus Budi
Journal Research of Social Science, Economics, and Management Vol. 4 No. 12 (2025): Journal Research of Social Science, Economics, and Management
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jrssem.v4i12.925

Abstract

Upstream oil and gas companies face challenges in managing transformer asset data that is vital to operations. The complexity of manual data and transformer information makes it difficult to make optimal decisions. This study aims to develop an effective transformer asset management dashboard using FSN (Fast-Slow-Non Moving) and ABC (Analysis Based Costing) analysis. The research methodology includes identifying system needs, collecting data through observation and interviews, developing solutions using Microsoft Power BI, and testing the User Acceptance Test (UAT). The results showed that the dashboard successfully integrated transformer data with real-time visualization including availability status (2,124 units available, 4,305 ready line units, 2,181 units not ready), ABC-FSN classification (AF 30%, BS 20%, AS 10%, CN 40%), and monthly stock trends. The UAT evaluation of 17 respondents showed an average score of 4.5 with a very appropriate category in the aspects of functional suitability, usability, performance efficiency, reliability, accuracy, visual design, and user satisfaction. Dashboards integrated with the Microsoft 365 ecosystem enable real-time access and support data-driven decision-making for transformer asset management optimization.
Prediction of OPC Cement Compressive Strength Based on Cement Chemical and Physical Parameters Using Machine Learning Techniques Ramadhan, Syahrial; Raharjo, Agus Budi
Journal Research of Social Science, Economics, and Management Vol. 4 No. 12 (2025): Journal Research of Social Science, Economics, and Management
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jrssem.v4i12.959

Abstract

This study aims to develop machine learning models to predict the 28-day compressive strength of Ordinary Portland Cement (OPC) based on chemical and physical parameters. The ultra-competitive cement industry requires companies to innovate continuously, but the conventional testing process takes at least 28 days, making product customization inefficient. This research proposes using machine learning techniques to accelerate this process. The predictive parameters include chemical components (C3S, C2S, C4AF, SiO2, etc.) and physical properties (Blaine, Residue, LOI, etc.) of OPC cement. The modeling was performed using random forest, gradient boosting, and artificial neural network algorithms. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared values. The study used 1,570 valid data points from cement quality testing at PT Semen Gresik. Results show that the random forest method provides the highest coefficient of determination of 0.856 with RMSE of 13.086 kg/cm² and MAE of 10.784 kg/cm². The most significant attributes affecting prediction are CaO, Insol, SiO2, MgO, Al2O3, and SO3. Performance can be further enhanced through hyperparameter tuning using grid search method, achieving a coefficient of determination of 0.976 with RMSE of 6.118 kg/cm² and MAE of 5.198 kg/cm². This research contributes to accelerating cement quality control processes and supports faster product development in the cement industry.
Co-Authors Aji, Adam Sekti Arifiani, Siska Diana Purwitasari Dwi Heppy Rochmawati Dwi Sunaryono Fabroyir, Hadziq Fajar Baskoro Maheswari, Clarissa Luna Mohamed Quafafou Nugraha, Raditya Hari Purnomo - Ramadhan, Syahrial Ridho Rahman Hariadi Susanti, Lestari Indah Wakhid, Muhammad Abdul Yorinda, Ilham Achmadi