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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi Karya Ilmiah S.1 Ilmu Keperawatan Jurnal Teknologi Informasi dan Ilmu Komputer Briliant: Jurnal Riset dan Konseptual Business and Finance Journal Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Teknik Informatika (JUTIF) Journal Research of Social Science, Economics, and Management Sewagati
Agus Budi Raharjo
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Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Dentistry Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Nursing Public Health Social Sciences Other

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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.
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.
Identifikasi Pengaruh Pandemi Covid-19 terhadap Perilaku Pengguna Twitter dengan Pendekatan Social Network Analysis Purwitasari, Diana; Apriantoni, Apriantoni; Raharjo, Agus Budi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 6: Desember 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021865213

Abstract

Pandemi COVID-19 yang berlangsung lama telah berdampak masif pada berbagai aktivitas publik, misalnya perilaku pengguna di media sosial. Twitter, media sosial yang fleksibel untuk berdiskusi dan bertukar pendapat, menjadi salah satu media populer dalam menyebarluaskan informasi COVID-19 secara dinamis dan up-to-date. Hal ini menjadikan twitter relevan sebagai media ekstraksi pengetahuan dalam mengidentifikasi perubahan perilaku pengguna. Kontribusi penelitian ini adalah menemukan perubahan perilaku pengguna twitter melalui analisis profil pengguna pada periode sebelum dan setelah COVID-19. Data yang digunakan adalah data tweet berbahasa Indonesia. Penelitian ini menggunakan pendekatan Social Network Analysis (SNA) sebagai ekstraksi informasi dalam menentukan aktor utama dan aktor populer. Kemudian, profil pengguna aktif dianalisis untuk mengidentifikasi perubahan perilaku melalui intensitas tweet, popularitas pengguna, dan representasi topik pembahasan. Popularitas pengguna dianalisis dengan pendekatan follower rank, sedangkan representasi topik pembahasan diekstraksi dengan metode Latent Dirichlet Allocation untuk mendapatkan dominan topik yang dibahas oleh setiap pengguna aktif. Tujuannya adalah untuk mempermudah  identifikasi pengaruh pandemi COVID-19 terhadap perubahan perilaku pengguna twitter. Berdasarkan hasil SNA, penelitian ini menemukan tiga aktor  kunci yang aktif pada periode sebelum dan setelah COVID-19. Selanjutnya, hasil analisis dari ketiga aktor tersebut menunjukkan adanya pengaruh pandemi COVID-19 terhadap perubahan perilaku pengguna twitter, yaitu kenaikan intensitas tweet sebesar 58% pada jam kerja, aktor utama yang didominasi oleh 60% pengguna dengan follower rendah, dan topik pembicaraan pengguna twitter yang dominan membahas COVID-19, hobi dan aktivitas di dalam rumah. AbstractThe long-lasting COVID-19 pandemic had a massive impact on public activities, such as user behavior on social media. Twitter, a flexible social media for discussing and exchanging opinions, has become popular in disseminating COVID-19  dynamic and up-to-date information. It makes twitter relevant as a medium of knowledge extraction in identifying user behavior changes. The contribution of this research is to find behavior changes of Twitter users through user profiles analysis in the before and after COVID-19 period. This data used is Indonesian-language tweets. This research used a Social Network Analysis (SNA) to determine the main actors and famous actors. Then, active user profiles were analyzed to identify behavior changes through tweet intensity, user popularity, and representation of the topic of discussion. User popularity was analyzed using a follower rank approach. At the same time, the representation of discussion topics was extracted using the Latent Dirichlet Allocation method to obtain dominant topics which each active user discusses. It aims to make it easier to identify the impact of the COVID-19 pandemic on Twitter user behavior changes. Based on the results of the SNA, this research found three key actors who were active in the before and after COVID-19 period. Then, the results of the analysis of these three user profiles shows that an influence of the COVID-19 pandemic on Twitter user behavior changes: an increase in tweet intensity by 58% during working hours, the leading actor was dominated by 60% of users with low followers, and the topic of Twitter users' conversation that it dominantly discuss COVID-19 issues, hobbies, and activities at home.
Jaringan Komunitas Berbasis Similaritas Topik Bahasan dan Emosi untuk Mengidentifikasi Perilaku Pengguna Twitter Apriantoni, Apriantoni; Purwitasari, Diana; Raharjo, Agus Budi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 1: Februari 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023106317

Abstract

Pandemi COVID-19 menyebabkan situasi krisis yang berdampak pada perubahan perilaku pengguna Twitter terkait pengalaman distres publik. Perubahan perilaku positif bisa berdampak positif. Namun, perubahan perilaku negatif bisa menjadi masalah jika terjadi secara masif, seperti meningkatnya kecemasan pengguna. Oleh karena itu, mengeksplorasi hubungan antara perilaku dan jaringan komunitas pengguna sangat penting untuk menemukan implikasi pandemi COVID-19 terhadap perubahan perilaku pengguna Twitter. Penelitian ini berkontribusi dalam mengidentifikasi perubahan perilaku pengguna berdasarkan model ekstraksi perilaku kolektif pada aktivitas tweet temporal. Mekanisme ini menggunakan topik bahasan dan emosi sebagai variabel ekstraksi untuk menghasilkan jaringan perilaku pengguna. Kemudian, jaringan perilaku tersebut dimodelkan dengan algoritma DeepWalk Network Embeddings untuk memetakan hubungan kedekatan perilaku antar pengguna dan Density Peak Clustering Algorithm untuk mengelompokkan komunitas pengguna berdasarkan kesamaan perilaku yang kuat. Dari analisis 121 pengguna aktif, periode sebelum COVID-19 memiliki 98 pengguna representatif yang didominasi oleh 33% perilaku komunitas terkait aktivitas pribadi dengan emosi senang. Di sisi lain, periode setelah COVID-19 memiliki 54 pengguna representatif yang didominasi oleh 65% perilaku komunitas terkait kesehatan dengan emosi marah. Perubahan perilaku kedua periode tersebut dipengaruhi oleh transisi pola jaringan terdistribusi ke pola jaringan clique graph, sehingga sentralisasi penyebaran informasi mempengaruhi potensi peningkatan perubahan perilaku pengguna pada jaringan komunitas. Hasil ini dapat digunakan untuk mengurangi potensi penyebaran perilaku negatif dengan memanfaatkan komunitas yang memiliki pengaruh perilaku positif dikalangan pengguna Twitter. AbstractThe COVID-19 pandemic caused a crisis that impacted behavior changes of Twitter users related to public distress experiences. Positive behavior changes could have a positive impact. However, negative behavior changes could have problems if it occur massively, such as increased user anxiety. Therefore, exploring the relationship between behavior and user community in the social networks is very important to find the implication of the COVID-19 pandemic on behavior changes of Twitter users. This study contributes to identify user behavior changes based on the collective behavior extraction model on temporal tweet activities. This mechanism used discussion topics and emotions as extraction variables to generate user behavior network. Then, the behavioral network was modeled by the DeepWalk Network Embeddings algorithm to map the behavioral closeness relationship between users and the Density Peak Clustering Algorithm to group user communities with strong behavioral similarities. Based on the analysis of 121 active users, before the COVID-19 period had 98 representative users, who were dominated by 33% of community behavior related to personal activities with happy emotions. On the other hand, after the COVID-19 period, 54 representative users were dominated by 65% of community behavior related to health with anger. Behavior changes in both periods are influenced by the transition from a distributed network pattern to a clique graph network pattern, so the centralization of information dissemination could affect the potential for increasing user behavioral changes in the community network. These findings could be used to reduce the potential for spreading negative behavior by leveraging communities with positive behavior influence among Twitter users.
ANALYSIS OF RAW MATERIAL INVENTORY PREDICTION FOR PLASTIC ORE USING A COMBINATION OF CAUSALITY AND TIME SERIES METHODS: A CASE STUDY IN A TEXTILE INDUSTRY COMPANY Frangky Rawung; Agus Budi Raharjo; Diana Purwitasari
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1809

Abstract

Raw material inventory is a valuable company asset in production activities. Inadequate or excessive availability can lead to production failures or cost wastage. This research aims to predict raw material inventory based on factors such as initial stock, receipts, usage, final stock, and differences in usage. A causality-based approach with Multiple Linear Regression (MLR) is used as the basis, complemented by a time series data approach that processes data trends using the Bidirectional Long Short-Term Memory (BiLSTM) algorithm. The prediction results from both models are then combined using the harmonic mean. This research utilizes a dataset of raw material inventory and applies the Root Mean Squared Error (RMSE) and R-squared (R²) performance parameters for model evaluation. The research is expected to provide useful information for companies in managing their raw material inventory and improving the efficiency of their production processes. Results show that, in the BiLSTM deep learning model, Polyethylene Terephthalate (PET) raw materials yielded an RMSE of 6.53 and an R² of 0.93. These results indicate that PET raw materials have a higher predictive value than other materials.
Co-Authors Aji, Adam Sekti Apriantoni, Apriantoni Ardianto Ardianto Budiyono, Yanuardhi Arief Diana Purwitasari Diana Purwitasari Dwi Heppy Rochmawati Dwi Sunaryono Fajar Baskoro Frangky Rawung Maheswari, Clarissa Luna Mohamed Quafafou Purnomo - Ramadhan, Syahrial Susanti, Lestari Indah Yorinda, Ilham Achmadi